{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "nRSlxT4q5EHJ"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import random\n",
        "import tensorflow as tf\n",
        "from google.colab import files\n",
        "from statsmodels.tsa.seasonal import seasonal_decompose\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Dense\n",
        "from keras.layers import LSTM\n",
        "from tensorflow.keras.preprocessing.sequence import TimeseriesGenerator\n"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 1. Upload file dari PC\n",
        "uploaded = files.upload()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 73
        },
        "id": "gaeeT_rKVXTW",
        "outputId": "62920dd2-9d02-4444-a9fb-1782ef764fee"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-46071371-f2c8-4c28-9cf4-dae47652c7cf\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-46071371-f2c8-4c28-9cf4-dae47652c7cf\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script>// Copyright 2017 Google LLC\n",
              "//\n",
              "// Licensed under the Apache License, Version 2.0 (the \"License\");\n",
              "// you may not use this file except in compliance with the License.\n",
              "// You may obtain a copy of the License at\n",
              "//\n",
              "//      http://www.apache.org/licenses/LICENSE-2.0\n",
              "//\n",
              "// Unless required by applicable law or agreed to in writing, software\n",
              "// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
              "// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
              "// See the License for the specific language governing permissions and\n",
              "// limitations under the License.\n",
              "\n",
              "/**\n",
              " * @fileoverview Helpers for google.colab Python module.\n",
              " */\n",
              "(function(scope) {\n",
              "function span(text, styleAttributes = {}) {\n",
              "  const element = document.createElement('span');\n",
              "  element.textContent = text;\n",
              "  for (const key of Object.keys(styleAttributes)) {\n",
              "    element.style[key] = styleAttributes[key];\n",
              "  }\n",
              "  return element;\n",
              "}\n",
              "\n",
              "// Max number of bytes which will be uploaded at a time.\n",
              "const MAX_PAYLOAD_SIZE = 100 * 1024;\n",
              "\n",
              "function _uploadFiles(inputId, outputId) {\n",
              "  const steps = uploadFilesStep(inputId, outputId);\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  // Cache steps on the outputElement to make it available for the next call\n",
              "  // to uploadFilesContinue from Python.\n",
              "  outputElement.steps = steps;\n",
              "\n",
              "  return _uploadFilesContinue(outputId);\n",
              "}\n",
              "\n",
              "// This is roughly an async generator (not supported in the browser yet),\n",
              "// where there are multiple asynchronous steps and the Python side is going\n",
              "// to poll for completion of each step.\n",
              "// This uses a Promise to block the python side on completion of each step,\n",
              "// then passes the result of the previous step as the input to the next step.\n",
              "function _uploadFilesContinue(outputId) {\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  const steps = outputElement.steps;\n",
              "\n",
              "  const next = steps.next(outputElement.lastPromiseValue);\n",
              "  return Promise.resolve(next.value.promise).then((value) => {\n",
              "    // Cache the last promise value to make it available to the next\n",
              "    // step of the generator.\n",
              "    outputElement.lastPromiseValue = value;\n",
              "    return next.value.response;\n",
              "  });\n",
              "}\n",
              "\n",
              "/**\n",
              " * Generator function which is called between each async step of the upload\n",
              " * process.\n",
              " * @param {string} inputId Element ID of the input file picker element.\n",
              " * @param {string} outputId Element ID of the output display.\n",
              " * @return {!Iterable<!Object>} Iterable of next steps.\n",
              " */\n",
              "function* uploadFilesStep(inputId, outputId) {\n",
              "  const inputElement = document.getElementById(inputId);\n",
              "  inputElement.disabled = false;\n",
              "\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  outputElement.innerHTML = '';\n",
              "\n",
              "  const pickedPromise = new Promise((resolve) => {\n",
              "    inputElement.addEventListener('change', (e) => {\n",
              "      resolve(e.target.files);\n",
              "    });\n",
              "  });\n",
              "\n",
              "  const cancel = document.createElement('button');\n",
              "  inputElement.parentElement.appendChild(cancel);\n",
              "  cancel.textContent = 'Cancel upload';\n",
              "  const cancelPromise = new Promise((resolve) => {\n",
              "    cancel.onclick = () => {\n",
              "      resolve(null);\n",
              "    };\n",
              "  });\n",
              "\n",
              "  // Wait for the user to pick the files.\n",
              "  const files = yield {\n",
              "    promise: Promise.race([pickedPromise, cancelPromise]),\n",
              "    response: {\n",
              "      action: 'starting',\n",
              "    }\n",
              "  };\n",
              "\n",
              "  cancel.remove();\n",
              "\n",
              "  // Disable the input element since further picks are not allowed.\n",
              "  inputElement.disabled = true;\n",
              "\n",
              "  if (!files) {\n",
              "    return {\n",
              "      response: {\n",
              "        action: 'complete',\n",
              "      }\n",
              "    };\n",
              "  }\n",
              "\n",
              "  for (const file of files) {\n",
              "    const li = document.createElement('li');\n",
              "    li.append(span(file.name, {fontWeight: 'bold'}));\n",
              "    li.append(span(\n",
              "        `(${file.type || 'n/a'}) - ${file.size} bytes, ` +\n",
              "        `last modified: ${\n",
              "            file.lastModifiedDate ? file.lastModifiedDate.toLocaleDateString() :\n",
              "                                    'n/a'} - `));\n",
              "    const percent = span('0% done');\n",
              "    li.appendChild(percent);\n",
              "\n",
              "    outputElement.appendChild(li);\n",
              "\n",
              "    const fileDataPromise = new Promise((resolve) => {\n",
              "      const reader = new FileReader();\n",
              "      reader.onload = (e) => {\n",
              "        resolve(e.target.result);\n",
              "      };\n",
              "      reader.readAsArrayBuffer(file);\n",
              "    });\n",
              "    // Wait for the data to be ready.\n",
              "    let fileData = yield {\n",
              "      promise: fileDataPromise,\n",
              "      response: {\n",
              "        action: 'continue',\n",
              "      }\n",
              "    };\n",
              "\n",
              "    // Use a chunked sending to avoid message size limits. See b/62115660.\n",
              "    let position = 0;\n",
              "    do {\n",
              "      const length = Math.min(fileData.byteLength - position, MAX_PAYLOAD_SIZE);\n",
              "      const chunk = new Uint8Array(fileData, position, length);\n",
              "      position += length;\n",
              "\n",
              "      const base64 = btoa(String.fromCharCode.apply(null, chunk));\n",
              "      yield {\n",
              "        response: {\n",
              "          action: 'append',\n",
              "          file: file.name,\n",
              "          data: base64,\n",
              "        },\n",
              "      };\n",
              "\n",
              "      let percentDone = fileData.byteLength === 0 ?\n",
              "          100 :\n",
              "          Math.round((position / fileData.byteLength) * 100);\n",
              "      percent.textContent = `${percentDone}% done`;\n",
              "\n",
              "    } while (position < fileData.byteLength);\n",
              "  }\n",
              "\n",
              "  // All done.\n",
              "  yield {\n",
              "    response: {\n",
              "      action: 'complete',\n",
              "    }\n",
              "  };\n",
              "}\n",
              "\n",
              "scope.google = scope.google || {};\n",
              "scope.google.colab = scope.google.colab || {};\n",
              "scope.google.colab._files = {\n",
              "  _uploadFiles,\n",
              "  _uploadFilesContinue,\n",
              "};\n",
              "})(self);\n",
              "</script> "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving data real bulanan 2019-2023 supremo.csv to data real bulanan 2019-2023 supremo.csv\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2fRF6mEW5QbK",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "f0881ba6-3661-41b7-834d-96d505d5e6b3"
      },
      "source": [
        "df = pd.read_csv('data real bulanan 2019-2023 supremo.csv', delimiter=';')\n",
        "df.head()"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Nomer  Tahun   Bulan  Produksi\n",
              "0      1   2019  Jan-19    170252\n",
              "1      2   2019  Feb-19    323468\n",
              "2      3   2019  Mar-19    616344\n",
              "3      4   2019  Apr-19    503524\n",
              "4      5   2019  May-19    277856"
            ],
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              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 60,\n  \"fields\": [\n    {\n      \"column\": \"Nomer\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 17,\n        \"min\": 1,\n        \"max\": 60,\n        \"num_unique_values\": 60,\n        \"samples\": [\n          1,\n          6,\n          37\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Tahun\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 2019,\n        \"max\": 2023,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          2020,\n          2023,\n          2021\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Bulan\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 60,\n        \"samples\": [\n          \"Jan-19\",\n          \"Jun-19\",\n          \"Jan-22\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Produksi\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 162557,\n        \"min\": 90528,\n        \"max\": 734748,\n        \"num_unique_values\": 60,\n        \"samples\": [\n          170252,\n          104272,\n          530084\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(9, 6))\n",
        "plt.plot(df.index, df['Produksi'], color='blue', marker='o', markersize=2, linewidth=2, alpha=0.7)\n",
        "plt.title('Data Produksi')\n",
        "plt.xlabel('Bulan')\n",
        "plt.ylabel('Produksi')\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "IctHV7x_M-vw",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 524
        },
        "outputId": "61e89784-c93c-4d69-f238-258f37cb0012"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 900x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Changed period to 12 for monthly data\n",
        "results = seasonal_decompose(df['Produksi'], model='additive', period=12)\n",
        "results.plot()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "id": "BuK5VqtkV064",
        "outputId": "edc8be4a-a5ca-4c58-8a54-e8c523440dd1"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 4 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Random Seed Untuk Reproduksi Data\n",
        "# Set random seeds di semua library\n",
        "seed_value = 42\n",
        "random.seed(seed_value)\n",
        "np.random.seed(seed_value)\n",
        "tf.random.set_seed(seed_value)"
      ],
      "metadata": {
        "id": "OGF2sq0XWQ6K"
      },
      "execution_count": 54,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1NoWcZEK6rPb"
      },
      "source": [
        "# Split data into training and testing sets\n",
        "train = df.iloc[:54].copy()\n",
        "test = df.iloc[54:].copy()"
      ],
      "execution_count": 55,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Prepocessing Data"
      ],
      "metadata": {
        "id": "86lxwp6PWwNS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Prepocessing Data with MinMaxScaler\n",
        "scaler = MinMaxScaler()\n",
        "scaled_train = scaler.fit_transform(train[['Produksi']])\n",
        "scaled_test = scaler.transform(test[['Produksi']])\n",
        "\n",
        "print(\"=== Data Test ===\")\n",
        "print(\"Train Original:\", train['Produksi'].head(3).values)\n",
        "print(\"Train Scaled:\", scaled_train[:3].flatten())"
      ],
      "metadata": {
        "id": "xdvMcAEm8b4q",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e22b033d-d9d7-4083-b5f8-84942f5df966"
      },
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "=== Data Test ===\n",
            "Train Original: [170252 323468 616344]\n",
            "Train Scaled: [0.12375276 0.36158455 0.81620564]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Model LSTM"
      ],
      "metadata": {
        "id": "NTKpVM7VXIQG"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CqyTKCVt69Z9"
      },
      "source": [
        "# define generator\n",
        "n_input = 12\n",
        "n_features = 1\n",
        "generator = TimeseriesGenerator(scaled_train, scaled_train, length=n_input, batch_size=1)"
      ],
      "execution_count": 57,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ip6y5Ene7OM7",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d495766f-4d12-435d-8a83-732253c38126"
      },
      "source": [
        "X,y = generator[0]\n",
        "print(f'Given the Array: \\n{X.flatten()}')\n",
        "print(f'Predict this y: \\n {y}')"
      ],
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Given the Array: \n",
            "[0.12375276 0.36158455 0.81620564 0.64107913 0.29078265 0.02133433\n",
            " 0.55775977 0.45020645 0.2241284  0.13337059 0.51362578 0.33075036]\n",
            "Predict this y: \n",
            " [[0.2472944]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bQyGmvsi8HyR",
        "outputId": "d3cefe04-0cdd-4b70-8ff1-5f31999f45a2"
      },
      "source": [
        "X.shape"
      ],
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1, 12, 1)"
            ]
          },
          "metadata": {},
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jm-T4H4I7UaV"
      },
      "source": [
        "# We do the same thing, but now instead for 6 months\n",
        "n_input = 6\n",
        "n_features = 1\n",
        "generator = TimeseriesGenerator(scaled_train, scaled_train, length=n_input, batch_size=1)"
      ],
      "execution_count": 96,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eJa-wFQf7jw6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "86684cde-f7df-420e-dce2-e9b2abac3260"
      },
      "source": [
        "# Set seeds for reproducibility\n",
        "np.random.seed(42)\n",
        "random.seed(42)\n",
        "tf.random.set_seed(42)\n",
        "\n",
        "# define model\n",
        "model = Sequential()\n",
        "model.add(LSTM(100, activation='relu', input_shape=(n_input, n_features)))\n",
        "model.add(Dense(1))\n",
        "model.compile(optimizer='adam', loss='mse')"
      ],
      "execution_count": 97,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.11/dist-packages/keras/src/layers/rnn/rnn.py:200: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
            "  super().__init__(**kwargs)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 197
        },
        "id": "7i4ucUNv7lbk",
        "outputId": "b06a253d-335c-4b06-b47f-4052965729fa"
      },
      "source": [
        "model.summary()"
      ],
      "execution_count": 98,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1mModel: \"sequential_4\"\u001b[0m\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential_4\"</span>\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
              "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
              "│ lstm_4 (\u001b[38;5;33mLSTM\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m100\u001b[0m)            │        \u001b[38;5;34m40,800\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ dense_4 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)              │           \u001b[38;5;34m101\u001b[0m │\n",
              "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
              "┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
              "│ lstm_4 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LSTM</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">100</span>)            │        <span style=\"color: #00af00; text-decoration-color: #00af00\">40,800</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ dense_4 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)              │           <span style=\"color: #00af00; text-decoration-color: #00af00\">101</span> │\n",
              "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m40,901\u001b[0m (159.77 KB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">40,901</span> (159.77 KB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m40,901\u001b[0m (159.77 KB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">40,901</span> (159.77 KB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "p8yqUZaz7nwp",
        "outputId": "d70a148c-1850-4ef2-bfac-ad1bc518f005"
      },
      "source": [
        "# fit model\n",
        "history = model.fit(generator,epochs=75)"
      ],
      "execution_count": 99,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/75\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.11/dist-packages/keras/src/trainers/data_adapters/py_dataset_adapter.py:121: UserWarning: Your `PyDataset` class should call `super().__init__(**kwargs)` in its constructor. `**kwargs` can include `workers`, `use_multiprocessing`, `max_queue_size`. Do not pass these arguments to `fit()`, as they will be ignored.\n",
            "  self._warn_if_super_not_called()\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m48/48\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 6ms/step - loss: 0.1260\n",
            "Epoch 2/75\n",
            "\u001b[1m48/48\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.1071\n",
            "Epoch 3/75\n",
            "\u001b[1m48/48\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 0.0690\n",
            "Epoch 4/75\n",
            "\u001b[1m48/48\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 0.0634\n",
            "Epoch 5/75\n",
            "\u001b[1m48/48\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.0750\n",
            "Epoch 6/75\n",
            "\u001b[1m48/48\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 0.0738\n",
            "Epoch 7/75\n",
            "\u001b[1m48/48\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 0.0610\n",
            "Epoch 8/75\n",
            "\u001b[1m48/48\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 0.0578\n",
            "Epoch 9/75\n",
            "\u001b[1m48/48\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.0766\n",
            "Epoch 10/75\n",
            "\u001b[1m48/48\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.0874\n",
            "Epoch 11/75\n",
            "\u001b[1m48/48\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 0.0648\n",
            "Epoch 12/75\n",
            "\u001b[1m48/48\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.0534\n",
            "Epoch 13/75\n",
            "\u001b[1m48/48\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.0512\n",
            "Epoch 14/75\n",
            "\u001b[1m48/48\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.0632\n",
            "Epoch 15/75\n",
            "\u001b[1m48/48\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.0566\n",
            "Epoch 16/75\n",
            "\u001b[1m48/48\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.0530\n",
            "Epoch 17/75\n",
            "\u001b[1m48/48\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.0610\n",
            "Epoch 18/75\n",
            "\u001b[1m48/48\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 10ms/step - loss: 0.0775\n",
            "Epoch 19/75\n",
            "\u001b[1m48/48\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 10ms/step - loss: 0.0547\n",
            "Epoch 20/75\n",
            "\u001b[1m48/48\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 10ms/step - loss: 0.0698\n",
            "Epoch 21/75\n",
            "\u001b[1m48/48\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - loss: 0.0590 \n",
            "Epoch 22/75\n",
            "\u001b[1m48/48\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.0584\n",
            "Epoch 23/75\n",
            "\u001b[1m48/48\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.0457\n",
            "Epoch 24/75\n",
            "\u001b[1m48/48\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 0.0549\n",
            "Epoch 25/75\n",
            "\u001b[1m48/48\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 0.0552\n",
            "Epoch 26/75\n",
            "\u001b[1m48/48\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 7ms/step - loss: 0.0489\n",
            "Epoch 27/75\n",
            "\u001b[1m48/48\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.0579\n",
            "Epoch 28/75\n",
            "\u001b[1m48/48\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 0.0476\n",
            "Epoch 29/75\n",
            "\u001b[1m48/48\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 0.0549\n",
            "Epoch 30/75\n",
            "\u001b[1m48/48\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 0.0630\n",
            "Epoch 31/75\n",
            "\u001b[1m48/48\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.0581\n",
            "Epoch 32/75\n",
            "\u001b[1m48/48\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.0636\n",
            "Epoch 33/75\n",
            "\u001b[1m48/48\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.0484\n",
            "Epoch 34/75\n",
            "\u001b[1m48/48\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 0.0536\n",
            "Epoch 35/75\n",
            "\u001b[1m48/48\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.0420\n",
            "Epoch 36/75\n",
            "\u001b[1m48/48\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 0.0661\n",
            "Epoch 37/75\n",
            "\u001b[1m48/48\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.0671\n",
            "Epoch 38/75\n",
            "\u001b[1m48/48\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.0568\n",
            "Epoch 39/75\n",
            "\u001b[1m48/48\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.0653\n",
            "Epoch 40/75\n",
            "\u001b[1m48/48\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.0614\n",
            "Epoch 41/75\n",
            "\u001b[1m48/48\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 0.0591\n",
            "Epoch 42/75\n",
            "\u001b[1m48/48\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.0600\n",
            "Epoch 43/75\n",
            "\u001b[1m48/48\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 0.0577\n",
            "Epoch 44/75\n",
            "\u001b[1m48/48\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 11ms/step - loss: 0.0606\n",
            "Epoch 45/75\n",
            "\u001b[1m48/48\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 11ms/step - loss: 0.0484\n",
            "Epoch 46/75\n",
            "\u001b[1m48/48\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 11ms/step - loss: 0.0443\n",
            "Epoch 47/75\n",
            "\u001b[1m48/48\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 0.0536\n",
            "Epoch 48/75\n",
            "\u001b[1m48/48\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.0601\n",
            "Epoch 49/75\n",
            "\u001b[1m48/48\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.0433\n",
            "Epoch 50/75\n",
            "\u001b[1m48/48\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 0.0531\n",
            "Epoch 51/75\n",
            "\u001b[1m48/48\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.0598\n",
            "Epoch 52/75\n",
            "\u001b[1m48/48\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.0518\n",
            "Epoch 53/75\n",
            "\u001b[1m48/48\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.0605\n",
            "Epoch 54/75\n",
            "\u001b[1m48/48\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.0555\n",
            "Epoch 55/75\n",
            "\u001b[1m48/48\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.0503\n",
            "Epoch 56/75\n",
            "\u001b[1m48/48\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.0489\n",
            "Epoch 57/75\n",
            "\u001b[1m48/48\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.0375\n",
            "Epoch 58/75\n",
            "\u001b[1m48/48\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 0.0451\n",
            "Epoch 59/75\n",
            "\u001b[1m48/48\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.0740\n",
            "Epoch 60/75\n",
            "\u001b[1m48/48\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.0639\n",
            "Epoch 61/75\n",
            "\u001b[1m48/48\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.0430\n",
            "Epoch 62/75\n",
            "\u001b[1m48/48\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.0532\n",
            "Epoch 63/75\n",
            "\u001b[1m48/48\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 0.0559\n",
            "Epoch 64/75\n",
            "\u001b[1m48/48\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 0.0440\n",
            "Epoch 65/75\n",
            "\u001b[1m48/48\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 0.0602\n",
            "Epoch 66/75\n",
            "\u001b[1m48/48\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.0461\n",
            "Epoch 67/75\n",
            "\u001b[1m48/48\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 0.0579\n",
            "Epoch 68/75\n",
            "\u001b[1m48/48\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.0374\n",
            "Epoch 69/75\n",
            "\u001b[1m48/48\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.0519\n",
            "Epoch 70/75\n",
            "\u001b[1m48/48\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.0439\n",
            "Epoch 71/75\n",
            "\u001b[1m48/48\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.0566\n",
            "Epoch 72/75\n",
            "\u001b[1m48/48\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - loss: 0.0503\n",
            "Epoch 73/75\n",
            "\u001b[1m48/48\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.0634\n",
            "Epoch 74/75\n",
            "\u001b[1m48/48\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 10ms/step - loss: 0.0468\n",
            "Epoch 75/75\n",
            "\u001b[1m48/48\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 12ms/step - loss: 0.0373\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 448
        },
        "id": "OjDsOKR67rs8",
        "outputId": "5c54f134-b0e8-4f88-9ac4-3fdcc10fa431"
      },
      "source": [
        "loss_per_epoch = model.history.history['loss']\n",
        "plt.plot(range(len(loss_per_epoch)),loss_per_epoch)"
      ],
      "execution_count": 100,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x78dc12b78ed0>]"
            ]
          },
          "metadata": {},
          "execution_count": 100
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h6h-fell70_S"
      },
      "source": [
        "last_train_batch = scaled_train[-6:]"
      ],
      "execution_count": 101,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cwwv8O-p79bN"
      },
      "source": [
        "last_train_batch = last_train_batch.reshape((1, n_input, n_features))"
      ],
      "execution_count": 102,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "v-4WD4t78MpJ",
        "outputId": "b37f00e9-2f60-421b-f9c5-5899aa905ca0"
      },
      "source": [
        "model.predict(last_train_batch)"
      ],
      "execution_count": 103,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 296ms/step\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.4187104]], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 103
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZbojFADE8StA",
        "outputId": "500dfa2a-7df5-4389-a50f-932aaf899c14"
      },
      "source": [
        "scaled_test[0]"
      ],
      "execution_count": 104,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.55599019])"
            ]
          },
          "metadata": {},
          "execution_count": 104
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_MP-5Wez8jbY",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a89baa66-e68b-4142-b10b-baed90ddf7a3"
      },
      "source": [
        "test_predictions = []\n",
        "\n",
        "first_eval_batch = scaled_train[-n_input:]\n",
        "current_batch = first_eval_batch.reshape((1, n_input, n_features))\n",
        "\n",
        "for i in range(len(test)):\n",
        "\n",
        "    # get the prediction value for the first batch\n",
        "    current_pred = model.predict(current_batch)[0]\n",
        "\n",
        "    # append the prediction into the array\n",
        "    test_predictions.append(current_pred)\n",
        "\n",
        "    # use the prediction to update the batch and remove the first value\n",
        "    current_batch = np.append(current_batch[:,1:,:],[[current_pred]],axis=1)"
      ],
      "execution_count": 105,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 82ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_mhvFuEy8sB7",
        "outputId": "52be4e7e-2aa5-42e1-fbb5-61925097f1f1"
      },
      "source": [
        "test_predictions"
      ],
      "execution_count": 106,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[array([0.4187104], dtype=float32),\n",
              " array([0.3766462], dtype=float32),\n",
              " array([0.3691223], dtype=float32),\n",
              " array([0.41722146], dtype=float32),\n",
              " array([0.36069882], dtype=float32),\n",
              " array([0.33168378], dtype=float32)]"
            ]
          },
          "metadata": {},
          "execution_count": 106
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "YYSqV10E9hlO",
        "outputId": "4a5db683-af30-4842-f3a3-daf3cb1ef04a"
      },
      "source": [
        "test.head()"
      ],
      "execution_count": 107,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    Nomer  Tahun   Bulan  Produksi    Predictions      Error\n",
              "54     55   2023  Jul-23    448708  417249.237978   7.010965\n",
              "55     56   2023  Aug-23    412512  405824.012626   1.621283\n",
              "56     57   2023  Sep-23    299914  436923.671597  45.682986\n",
              "57     58   2023  Oct-23    368213  432410.311406  17.434830\n",
              "58     59   2023  Nov-23    523068  377744.184824  27.782968"
            ],
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              "\n",
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              "        async function quickchart(key) {\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "test",
              "summary": "{\n  \"name\": \"test\",\n  \"rows\": 6,\n  \"fields\": [\n    {\n      \"column\": \"Nomer\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 55,\n        \"max\": 60,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          55,\n          56,\n          60\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Tahun\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 2023,\n        \"max\": 2023,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          2023\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Bulan\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 6,\n        \"samples\": [\n          \"Jul-23\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Produksi\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 135373,\n        \"min\": 299914,\n        \"max\": 686476,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          448708\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Predictions\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 29944.216336802205,\n        \"min\": 362335.46024250984,\n        \"max\": 436923.6715965271,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          417249.23797774315\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Error\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 19.260397874240432,\n        \"min\": 1.6212831078149674,\n        \"max\": 47.21804400408611,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          7.01096526521855\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 107
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BHHzjNDK9EJZ"
      },
      "source": [
        "true_predictions = scaler.inverse_transform(test_predictions)\n",
        "test['Predictions'] = true_predictions"
      ],
      "execution_count": 108,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Calculate the absolute difference between 'Produksi' and 'Predictions'\n",
        "test['Error'] = abs((test['Produksi'] - test['Predictions'])/test['Produksi'])*100\n",
        "\n",
        "# Display the updated DataFrame with the new 'Error' column\n",
        "test\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        },
        "id": "qX-ypp5gqywe",
        "outputId": "f4085944-5e63-4457-d2ea-b065b708d80e"
      },
      "execution_count": 109,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    Nomer  Tahun   Bulan  Produksi    Predictions      Error\n",
              "54     55   2023  Jul-23    448708  360269.620713  19.709562\n",
              "55     56   2023  Aug-23    412512  333171.009090  19.233620\n",
              "56     57   2023  Sep-23    299914  328323.965897   9.472704\n",
              "57     58   2023  Oct-23    368213  359310.406878   2.417783\n",
              "58     59   2023  Nov-23    523068  322897.393280  38.268563\n",
              "59     60   2023  Dec-23    686476  304205.327794  55.685949"
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              "type": "dataframe",
              "variable_name": "test",
              "summary": "{\n  \"name\": \"test\",\n  \"rows\": 6,\n  \"fields\": [\n    {\n      \"column\": \"Nomer\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 55,\n        \"max\": 60,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          55,\n          56,\n          60\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Tahun\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 2023,\n        \"max\": 2023,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          2023\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Bulan\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 6,\n        \"samples\": [\n          \"Jul-23\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Produksi\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 135373,\n        \"min\": 299914,\n        \"max\": 686476,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          448708\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Predictions\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 21779.328409399626,\n        \"min\": 304205.32779443264,\n        \"max\": 360269.62071347237,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          360269.62071347237\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Error\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 19.62370158733154,\n        \"min\": 2.417783490024706,\n        \"max\": 55.68594855545822,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          19.709561515847195\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 109
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# ... (Your existing plotting code) ...\n",
        "\n",
        "# Calculate MAPE (assuming you already have y_true and y_pred)\n",
        "def mean_absolute_percentage_error(y_true, y_pred):\n",
        "    y_true, y_pred = np.array(y_true), np.array(y_pred)\n",
        "    return np.mean(np.abs((y_true - y_pred) / y_true)) * 100\n",
        "\n",
        "y_true = test['Produksi'].values\n",
        "y_pred = test['Predictions'].values\n",
        "\n",
        "mape_final = mean_absolute_percentage_error(y_true, y_pred) #Calculate and assign mape_final\n",
        "\n",
        "# ... (Rest of your plotting code) ...\n",
        "\n",
        "mape_text = f\"MAPE: {mape_final:.2f}%\" #Now mape_final is defined\n",
        "\n",
        "plt.annotate(mape_text,\n",
        "             xy=(0.05, 0.95),\n",
        "             xycoords='axes fraction',\n",
        "             fontsize=12,\n",
        "             bbox=dict(boxstyle='round', facecolor='white', alpha=0.8))\n",
        "\n",
        "\n",
        "plt.figure(figsize=(14, 7))\n",
        "\n",
        "# Plot data aktual\n",
        "plt.plot(test.index, test['Produksi'],\n",
        "         label='Data Aktual',\n",
        "         color='blue',\n",
        "         marker='o',\n",
        "         markersize=5,\n",
        "         linewidth=2,\n",
        "         alpha=0.7)\n",
        "\n",
        "# Plot prediksi\n",
        "plt.plot(test.index, test['Predictions'],\n",
        "         label='Prediksi Model',\n",
        "         color='red',\n",
        "         linestyle='--',\n",
        "         marker='x',\n",
        "         markersize=6,\n",
        "         linewidth=2)\n",
        "\n",
        "# Tambahkan detail grafik\n",
        "plt.title('Perbandingan Data Aktual vs Prediksi Model', fontsize=16, pad=20)\n",
        "plt.xlabel('Tanggal', fontsize=12)\n",
        "plt.ylabel('Produksi', fontsize=12)\n",
        "plt.grid(True, which='both', linestyle='--', linewidth=0.5)\n",
        "plt.legend(fontsize=12)\n",
        "\n",
        "# Rotasi label tanggal jika terlalu padat\n",
        "plt.xticks(rotation=45)\n",
        "\n",
        "# Tambahkan anotasi MAPE\n",
        "mape_text = f\"MAPE: {mape_final:.2f}%\"\n",
        "plt.annotate(mape_text,\n",
        "             xy=(0.05, 0.95),\n",
        "             xycoords='axes fraction',\n",
        "             fontsize=12,\n",
        "             bbox=dict(boxstyle='round', facecolor='white', alpha=0.8))\n",
        "\n",
        "# Highlight area error\n",
        "plt.fill_between(test.index,\n",
        "                 test['Produksi'],\n",
        "                 test['Predictions'],\n",
        "                 color='orange',\n",
        "                 alpha=0.1,\n",
        "                 label='Error')\n",
        "\n",
        "plt.tight_layout()  # Adjust layout\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 802
        },
        "id": "fjvREAUvjxQw",
        "outputId": "58e65c80-11ab-4ec5-ccf0-6e46d247d652"
      },
      "execution_count": 110,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1400x700 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OCsYjzge9OVe",
        "outputId": "ea146c44-087e-4bcd-b3b3-1cb3bfbb3667"
      },
      "source": [
        "from sklearn.metrics import mean_squared_error\n",
        "from math import sqrt\n",
        "rmse=sqrt(mean_squared_error(test['Produksi'],test['Predictions']))\n",
        "print(f\"RMSE Model LSTM: {rmse:.2f}\")"
      ],
      "execution_count": 111,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "RMSE Model LSTM: 183121.94\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def mean_absolute_percentage_error(y_true, y_pred):\n",
        "    y_true, y_pred = np.array(y_true), np.array(y_pred)\n",
        "    return np.mean(np.abs((y_true - y_pred) / y_true)) * 100\n",
        "\n",
        "# Gunakan nilai sebenarnya dari data test dan prediksi model:\n",
        "y_true = test['Produksi'].values\n",
        "y_pred = test['Predictions'].values\n",
        "\n",
        "mape = mean_absolute_percentage_error(y_true, y_pred)\n",
        "print(f\"MAPE Model LSTM: {mape:.2f}%\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0y8tGeJgLFR8",
        "outputId": "2a561428-c925-4488-f5fc-28cb087b19ce"
      },
      "execution_count": 112,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MAPE Model LSTM: 24.13%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# prompt: evaluas mae-nya juga\n",
        "\n",
        "# Evaluasi MAE (Mean Absolute Error)\n",
        "from sklearn.metrics import mean_absolute_error\n",
        "\n",
        "mae = mean_absolute_error(y_true, y_pred)\n",
        "print(f\"MAE Model LSTM: {mae:.2f}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YMHlVmMH-vyM",
        "outputId": "45225501-fa72-4405-a6d3-176ff4355c33"
      },
      "execution_count": 113,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MAE Model LSTM: 131255.53\n"
          ]
        }
      ]
    }
  ]
}