{"id":50932,"date":"2025-03-21T00:00:00","date_gmt":"2025-03-21T07:00:00","guid":{"rendered":"https:\/\/griddb-linux-hte8hndjf8cka8ht.westus-01.azurewebsites.net\/%e6%9c%aa%e5%88%86%e9%a1%9e\/time-series-classification-with-amazon-chronos-model-with-griddb\/"},"modified":"2025-11-14T07:57:06","modified_gmt":"2025-11-14T15:57:06","slug":"time-series-classification-with-amazon-chronos-model-with-griddb","status":"publish","type":"post","link":"https:\/\/www.griddb.net\/ja\/%e6%9c%aa%e5%88%86%e9%a1%9e\/time-series-classification-with-amazon-chronos-model-with-griddb\/","title":{"rendered":"Amazon Chronos \u30e2\u30c7\u30eb\u3068 GridDB \u3092\u4f7f\u7528\u3057\u305f\u6642\u7cfb\u5217\u5206\u985e"},"content":{"rendered":"

\u3053\u306e\u8a18\u4e8b\u3067\u306f\u3001Amazon Chronos<\/a>\u3068GridDB<\/a>\u30c7\u30fc\u30bf\u30d9\u30fc\u30b9\u3092\u4f7f\u7528\u3057\u3066\u3001\u96fb\u529b\u751f\u7523\u306e\u30bf\u30a4\u30e0\u30b7\u30ea\u30fc\u30ba\u4e88\u6e2c\u30e2\u30c7\u30eb\u3092\u69cb\u7bc9\u3059\u308b\u65b9\u6cd5\u3092\u8aac\u660e\u3057\u307e\u3059\u3002<\/p>\n

Kaggle\u304b\u3089\u904e\u53bb\u306e\u96fb\u529b\u751f\u7523\u30c7\u30fc\u30bf\u3092\u53d6\u5f97\u3057\u3001GridDB\u306e\u30bf\u30a4\u30e0\u30b7\u30ea\u30fc\u30ba\u30b3\u30f3\u30c6\u30ca\u306b\u683c\u7d0d\u3057\u307e\u3059\u3002\u305d\u306e\u5f8c\u3001T5\u30a2\u30fc\u30ad\u30c6\u30af\u30c1\u30e3\u3092\u57fa\u76e4\u3068\u3057\u305f\u30bf\u30a4\u30e0\u30b7\u30ea\u30fc\u30ba\u30e2\u30c7\u30eb\u5c02\u7528\u306e\u30b3\u30ec\u30af\u30b7\u30e7\u30f3\u3067\u3042\u308bAmazon Chronos\u3092\u4f7f\u7528\u3057\u3066\u3001\u4e88\u6e2c\u30e2\u30c7\u30eb\u3092\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0\u3057\u307e\u3059\u3002<\/p>\n

GridDB\u306f\u3001\u5927\u898f\u6a21\u306a\u30ea\u30a2\u30eb\u30bf\u30a4\u30e0\u30c7\u30fc\u30bf\u306e\u51e6\u7406\u306b\u6700\u9069\u5316\u3055\u308c\u305f\u5805\u7262\u306aNoSQL\u30c7\u30fc\u30bf\u30d9\u30fc\u30b9\u3067\u3059\u3002\u9ad8\u5ea6\u306a\u30a4\u30f3\u30e1\u30e2\u30ea\u51e6\u7406\u3068\u6642\u7cfb\u5217\u30c7\u30fc\u30bf\u7ba1\u7406\u6a5f\u80fd\u306b\u3088\u308a\u3001\u30d3\u30c3\u30b0\u30c7\u30fc\u30bf\u3084IoT\u30a2\u30d7\u30ea\u30b1\u30fc\u30b7\u30e7\u30f3\u306b\u6700\u9069\u3067\u3059\u3002<\/p>\n

GridDB\u306e\u30ea\u30a2\u30eb\u30bf\u30a4\u30e0\u30c7\u30fc\u30bf\u51e6\u7406\u6a5f\u80fd\u3068Chronos\u306e\u6700\u5148\u7aef\u4e88\u6e2c\u624b\u6cd5\u306f\u3001\u6642\u7cfb\u5217\u4e88\u6e2c\u30a2\u30d7\u30ea\u30b1\u30fc\u30b7\u30e7\u30f3\u306b\u304a\u3044\u3066\u5f37\u529b\u306a\u7d44\u307f\u5408\u308f\u305b\u3068\u306a\u308a\u307e\u3059\u3002<\/p>\n

\u4e8b\u524d\u6e96\u5099<\/h2>\n

\u3053\u306e\u8a18\u4e8b\u306e\u30b3\u30fc\u30c9\u3092\u5b9f\u884c\u3059\u308b\u306b\u306f\u3001\u4ee5\u4e0b\u306e\u30e9\u30a4\u30d6\u30e9\u30ea\u3092\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u3059\u308b\u5fc5\u8981\u304c\u3042\u308a\u307e\u3059\u3002<\/p>\n

    \n
  1. GridDB C\u30af\u30e9\u30a4\u30a2\u30f3\u30c8<\/li>\n
  2. GridDB Python\u30af\u30e9\u30a4\u30a2\u30f3\u30c8<\/li>\n<\/ol>\n

    \u3053\u308c\u3089\u306e\u30af\u30e9\u30a4\u30a2\u30f3\u30c8\u306e\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u624b\u9806\u306f\u3001\u4ee5\u4e0b\u306e\u30ea\u30f3\u30af\u304b\u3089\u78ba\u8a8d\u3067\u304d\u307e\u3059\u3002 GridDB Python \u30d1\u30c3\u30b1\u30fc\u30b8 \u30a4\u30f3\u30c7\u30c3\u30af\u30b9<\/a>.<\/p>\n

    Amazon Chronos\u3001Numpy\u3001Pandas\u3001\u304a\u3088\u3073Matplotlib\u306e\u30e9\u30a4\u30d6\u30e9\u30ea\u3092\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u3059\u308b\u5fc5\u8981\u304c\u3042\u308a\u307e\u3059\u3002<\/p>\n

    \u4ee5\u4e0b\u306e\u30b9\u30af\u30ea\u30d7\u30c8\u306f\u3001\u30b3\u30fc\u30c9\u3092\u5b9f\u884c\u3059\u308b\u305f\u3081\u306b\u5fc5\u8981\u306a\u30e9\u30a4\u30d6\u30e9\u30ea\u3092\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u3057\u3001\u30a4\u30f3\u30dd\u30fc\u30c8\u3059\u308b\u306e\u3092\u52a9\u3051\u307e\u3059\u3002<\/p>\n

    \n
    %pip install git+https:\/\/github.com\/amazon-science\/chronos-forecasting.git\n%pip install matplotlib seaborn numpy pandas scikit-learn<\/code><\/pre>\n<\/div>\n
    \n
    import matplotlib.pyplot as plt\nfrom matplotlib.dates import DateFormatter\nimport seaborn as sns\nimport numpy as np\nimport pandas as pd\nimport torch\nfrom chronos import ChronosPipeline\nimport griddb_python as griddb\nfrom sklearn.metrics import mean_absolute_error<\/code><\/pre>\n<\/div>\n
    \u30b0\u30ea\u30c3\u30c9DB\u306b\u6642\u7cfb\u5217\u30c7\u30fc\u30bf\u3092\u633f\u5165\u3059\u308b<\/h2>\n
    \u6700\u521d\u306e\u30b9\u30c6\u30c3\u30d7\u306f\u3001\u4e88\u6e2c\u5bfe\u8c61\u306e\u6642\u7cfb\u5217\u30c7\u30fc\u30bf\u3092\u30b0\u30ea\u30c3\u30c9DB\u306b\u633f\u5165\u3059\u308b\u3053\u3068\u3067\u3059\u3002\u3053\u306e\u30bb\u30af\u30b7\u30e7\u30f3\u3067\u306f\u3001\u305d\u306e\u624b\u9806\u3092\u8aac\u660e\u3057\u307e\u3059\u3002<\/p>\n
    Kaggle\u304b\u3089\u96fb\u529b\u751f\u7523\u30c7\u30fc\u30bf\u3092\u30c0\u30a6\u30f3\u30ed\u30fc\u30c9\u3057\u3066\u30a4\u30f3\u30dd\u30fc\u30c8\u3059\u308b<\/h3>\n
    \u79c1\u305f\u3061\u306f\u3001Kaggle\u304b\u3089\u306e\u96fb\u529b\u751f\u7523\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8<\/a>\u3092\u4f7f\u7528\u3057\u3066\u3001\u5c06\u6765\u306e\u96fb\u529b\u751f\u7523\u9700\u8981\u3092\u4e88\u6e2c\u3057\u307e\u3059\u3002<\/p>\n
    \u4ee5\u4e0b\u306e\u30b9\u30af\u30ea\u30d7\u30c8\u306f\u3001\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u3092\u542b\u3080CSV\u30d5\u30a1\u30a4\u30eb\u3092pandas DataFrame\u306b\u30a4\u30f3\u30dd\u30fc\u30c8\u3057\u307e\u3059\u3002<\/p>\n
    \n
    dataset = pd.read_csv(\"Electric_Production.csv\")\ndataset.head(10)<\/code><\/pre>\n<\/div>\n
    \u51fa\u529b\uff1a<\/strong><\/p>\n
    \"\"<\/a><\/p>\n
    \u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306f\u30011985\u5e741\u67081\u65e5\u304b\u30892018\u5e741\u67081\u65e5\u307e\u3067\u306e\u6708\u6b21\u96fb\u529b\u751f\u7523\u91cf\u304b\u3089\u69cb\u6210\u3055\u308c\u3066\u3044\u307e\u3059\u3002<\/p>\n
    \u7dda\u30b0\u30e9\u30d5\u3092\u4f5c\u6210\u3059\u308b\u3053\u3068\u3067\u3001\u96fb\u529b\u751f\u7523\u91cf\u304c\u5e74\u306e\u6708\u306b\u3088\u3063\u3066\u5927\u304d\u304f\u7570\u306a\u308b\u3053\u3068\u304c\u78ba\u8a8d\u3067\u304d\u307e\u3059\u3002<\/p>\n
    \n
    # Create the line plot\nsns.set_style(\"darkgrid\")\nplt.figure(figsize=(12, 7))\nsns.lineplot(data=dataset, x='DATE', y='IPG2211A2N', label='Electricity Production')\nplt.xlabel('Date')\nplt.ylabel('Electricity Production (IPG2211A2N)')\nplt.title('Electricity Production Over Time')\nplt.legend()\n\nplt.show()<\/code><\/pre>\n<\/div>\n
    \u51fa\u529b\uff1a<\/strong><\/p>\n
    \"\"<\/a><\/p>\n
    \u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u304c\u7528\u610f\u3067\u304d\u305f\u3089\u3001\u3053\u306e\u30c7\u30fc\u30bf\u3092GridDB\u306b\u633f\u5165\u3067\u304d\u307e\u3059\u3002<\/p>\n
    GridDB\u3078\u306e\u63a5\u7d9a<\/h3>\n
    GridDB\u306b\u63a5\u7d9a\u3059\u308b\u306b\u306f\u3001StoreFactory<\/code>\u30af\u30e9\u30b9\u306e\u30aa\u30d6\u30b8\u30a7\u30af\u30c8\u3092\u4f5c\u6210\u3059\u308b\u5fc5\u8981\u304c\u3042\u308a\u307e\u3059\u3002\u6b21\u306b\u3001\u30b9\u30c8\u30a2\u30d5\u30a1\u30af\u30c8\u30ea\u30aa\u30d6\u30b8\u30a7\u30af\u30c8\u306eget_store\uff08\uff09<\/code>\u30e1\u30bd\u30c3\u30c9\u3092\u547c\u3073\u51fa\u3057\u3001DB\u30db\u30b9\u30c8\u540d\u3001\u30af\u30e9\u30b9\u30bf\u30fc\u540d\u3001\u30e6\u30fc\u30b6\u30fc\u540d\u3001\u30d1\u30b9\u30ef\u30fc\u30c9\u3092\u5f15\u6570\u3068\u3057\u3066\u6e21\u3057\u307e\u3059\u3002<\/p>\n
    \u63a5\u7d9a\u304c\u6b63\u5e38\u306b\u78ba\u7acb\u3055\u308c\u305f\u304b\u3069\u3046\u304b\u3092\u78ba\u8a8d\u3059\u308b\u306b\u306f\u3001get_container\uff08\uff09<\/code>\u30e1\u30bd\u30c3\u30c9\u3092\u547c\u3073\u51fa\u3057\u3001\u4efb\u610f\u306e\u30b3\u30f3\u30c6\u30ca\u540d\u3092\u5f15\u6570\u3068\u3057\u3066\u6e21\u3057\u307e\u3059\u3002\u4ee5\u4e0b\u306e\u51fa\u529b\u304c\u8868\u793a\u3055\u308c\u308c\u3070\u3001\u63a5\u7d9a\u306f\u6210\u529f\u3057\u3066\u3044\u307e\u3059\u3002<\/p>\n
    \n
    # GridDB connection details\nDB_HOST = \"127.0.0.1:10001\"\nDB_CLUSTER = \"myCluster\"\nDB_USER = \"admin\"\nDB_PASS = \"admin\"\n\n# creating a connection\n\nfactory = griddb.StoreFactory.get_instance()\n\ntry:\n    gridstore = factory.get_store(\n        notification_member = DB_HOST,\n        cluster_name = DB_CLUSTER,\n        username = DB_USER,\n        password = DB_PASS\n    )\n\n    container1 = gridstore.get_container(\"container1\")\n    if container1 == None:\n        print(\"Container does not exist\")\n    print(\"Successfully connected to GridDB\")\n\nexcept griddb.GSException as e:\n    for i in range(e.get_error_stack_size()):\n        print(\"[\", i, \"]\")\n        print(e.get_error_code(i))\n        print(e.get_location(i))\n        print(e.get_message(i))\n<\/code><\/pre>\n<\/div>\n
    \u51fa\u529b:<\/strong><\/p>\n
    \n
    Container does not exist\nSuccessfully connected to GridDB<\/code><\/pre>\n<\/div>\n
    \u30b0\u30ea\u30c3\u30c9DB\u306b\u96fb\u529b\u751f\u7523\u30c7\u30fc\u30bf\u7528\u306e\u30b3\u30f3\u30c6\u30ca\u3092\u4f5c\u6210\u3059\u308b<\/h3>\n
    \u30b0\u30ea\u30c3\u30c9DB\u306f\u30c7\u30fc\u30bf\u30b3\u30f3\u30c6\u30ca\u3092\u683c\u7d0d\u3057\u307e\u3059\u3002\u30b3\u30f3\u30c6\u30ca\u3092\u4f5c\u6210\u3059\u308b\u306b\u306f\u3001\u30b3\u30f3\u30c6\u30ca\u540d\u3068\u5217\u306e\u60c5\u5831\u304c\u5fc5\u8981\u3067\u3059\u3002<\/p>\n
    \u30b3\u30f3\u30c6\u30ca\u306b\u306f\u4efb\u610f\u306e\u540d\u524d\u3092\u4ed8\u3051\u308b\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002\u305f\u3060\u3057\u3001\u30b3\u30f3\u30c6\u30ca\u60c5\u5831\u306f\u3001\u5404\u30cd\u30b9\u30c8\u3055\u308c\u305f\u30ea\u30b9\u30c8\u304c\u5217\u540d\u3068\u5217\u30bf\u30a4\u30d7\u3092\u542b\u3080\u30ea\u30b9\u30c8\u306e\u30ea\u30b9\u30c8\u3067\u69cb\u6210\u3055\u308c\u308b\u5fc5\u8981\u304c\u3042\u308a\u307e\u3059\u3002<\/p>\n
    \u4f8b\u3048\u3070\u3001\u4ee5\u4e0b\u306e\u30b9\u30af\u30ea\u30d7\u30c8\u3067\u306f2\u3064\u306e\u5217\u304c\u3042\u308a\u307e\u3059\uff1aTimeStamp<\/code>\uff08\u5217\u30bf\u30a4\u30d7 griddb.Type.TIMESTAP<\/code>\uff09\u3068 Production<\/code>\uff08\u5217\u30bf\u30a4\u30d7 griddb.Type.DOUBLE<\/code>\uff09\u3002<\/p>\n
    \u6b21\u306b\u3001ContainerInfo<\/code> \u30af\u30e9\u30b9\u306e\u30aa\u30d6\u30b8\u30a7\u30af\u30c8\u3092\u4f5c\u6210\u3057\u3001\u30b3\u30f3\u30c6\u30ca\u540d\u3068\u5217\u60c5\u5831\u3092 ContainerInfo<\/code> \u30af\u30e9\u30b9\u306e\u30b3\u30f3\u30b9\u30c8\u30e9\u30af\u30bf\u306b\u6e21\u3057\u3057\u307e\u3059\u3002<\/p>\n
    \u6700\u5f8c\u306b\u3001put_container\uff08\uff09<\/code> \u30e1\u30bd\u30c3\u30c9\u3092\u547c\u3073\u51fa\u3057\u3001ContainerInfo<\/code> \u30af\u30e9\u30b9\u306e\u30aa\u30d6\u30b8\u30a7\u30af\u30c8\u3092\u5f15\u6570\u3068\u3057\u3066\u6e21\u3059\u3053\u3068\u3067\u3001GridDB \u306b\u30b3\u30f3\u30c6\u30ca\u3092\u4f5c\u6210\u3057\u307e\u3059\u3002<\/p>\n
    \n
    dataset['DATE'] = pd.to_datetime(dataset['DATE'])\n\n\ncontainer_name = \"Electricity_Production\"\ncolumn_info = [\n    [\"Timestamp\", griddb.Type.TIMESTAMP],\n    [\"Production\", griddb.Type.DOUBLE]\n]\ncontainer_info = griddb.ContainerInfo(container_name, column_info, griddb.ContainerType.TIME_SERIES)\n\n# Creating Container\ntry:\n    gridstore.put_container(container_info)\n    container = gridstore.get_container(container_name)\n    if container is None:\n        print(f\"Failed to create container: {container_name}\")\n    else:\n        print(f\"Successfully created container: {container_name}\")\n\nexcept griddb.GSException as e:\n    print(f\"Error creating or retrieving container {container_name}:\")\n    for i in range(e.get_error_stack_size()):\n        print(f\"[{i}]\")\n        print(f\"Error code: {e.get_error_code(i)}\")\n        print(f\"Location: {e.get_location(i)}\")\n        print(f\"Message: {e.get_message(i)}\")\n<\/code><\/pre>\n<\/div>\n
    \u51fa\u529b<\/strong><\/p>\n
    \n
    Successfully created container: Electricity_Production<\/code><\/pre>\n<\/div>\n
    \u4f5c\u6210\u3057\u305f\u30b3\u30f3\u30c6\u30ca\u306f\u3001get_container\uff08\uff09<\/code> \u30e1\u30bd\u30c3\u30c9\u3092\u4f7f\u7528\u3057\u3066\u53d6\u5f97\u3067\u304d\u307e\u3059\u3002<\/p>\n
    \u6b21\u306b\u3001\u4f5c\u6210\u3057\u305f\u30b3\u30f3\u30c6\u30ca\u306b\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u3092\u683c\u7d0d\u3057\u307e\u3059\u3002<\/p>\n
    \u96fb\u529b\u751f\u7523\u30c7\u30fc\u30bf\u3092GridDB\u30b3\u30f3\u30c6\u30ca\u306b\u633f\u5165<\/h3>\n
    GridDB\u30b3\u30f3\u30c6\u30ca\u306b\u30c7\u30fc\u30bf\u3092\u633f\u5165\u3059\u308b\u306b\u306f\u3001\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306e\u3059\u3079\u3066\u306e\u884c\u3092\u53cd\u5fa9\u51e6\u7406\u3057\u3001\u30b3\u30f3\u30c6\u30ca\u30aa\u30d6\u30b8\u30a7\u30af\u30c8\u306eput\uff08\uff09<\/code>\u30e1\u30bd\u30c3\u30c9\u3092\u547c\u3073\u51fa\u3057\u3001DATE<\/code>\u3068IPG2211A2N<\/code>\u5217\u306e\u5024\u3092\u30e1\u30bd\u30c3\u30c9\u306b\u6e21\u3057\u3057\u307e\u3059\u3002<\/p>\n
    \n
    try:\n    for index, row in dataset.iterrows():\n        container.put([row['DATE'].to_pydatetime(), row['IPG2211A2N']])\n    print(f\"Successfully inserted {len(dataset)} rows of data into {container_name}\")\n\nexcept griddb.GSException as e:\n    print(f\"Error inserting data into container {container_name}:\")\n    for i in range(e.get_error_stack_size()):\n        print(f\"[{i}]\")\n        print(f\"Error code: {e.get_error_code(i)}\")\n        print(f\"Location: {e.get_location(i)}\")\n        print(f\"Message: {e.get_message(i)}\")\n<\/code><\/pre>\n<\/div>\n
    \u51fa\u529b<\/strong><\/p>\n
    \n
    Successfully inserted 397 rows of data into Electricity_Production<\/code><\/pre>\n<\/div>\n
    \u96fb\u6c17\u767a\u96fb\u30c7\u30fc\u30bf\u3092GridDB\u306b\u6b63\u5e38\u306b\u633f\u5165\u3057\u307e\u3057\u305f\u3002\u6b21\u306e\u30b9\u30c6\u30c3\u30d7\u306f\u3001Amazon\u306eChronos\u30e2\u30c7\u30eb\u3092\u4f7f\u7528\u3057\u3066\u96fb\u6c17\u767a\u96fb\u91cf\u3092\u4e88\u6e2c\u3059\u308b\u3053\u3068\u3067\u3059\u3002<\/p>\n
    Amazon\u306eChronos\u6642\u7cfb\u5217\u30e2\u30c7\u30eb\u3092\u4f7f\u7528\u3057\u305f\u96fb\u6c17\u767a\u96fb\u91cf\u306e\u4e88\u6e2c<\/h2>\n
    Amazon Chronos\u306f\u3001\u4e8b\u524d\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0\u6e08\u307f\u306e\u8a00\u8a9e\u30e2\u30c7\u30eb\u7fa4\u3067\u3059<\/a>\u3002\u3053\u308c\u3089\u306e\u30e2\u30c7\u30eb\u306f\u3001\u6642\u7cfb\u5217\u30c7\u30fc\u30bf\u306b\u5bfe\u5fdc\u3059\u308b\u3088\u3046\u306b\u9069\u5fdc\u3055\u308c\u305fT5\uff08Text-to-Text Transfer Transformer\uff09\u30a2\u30fc\u30ad\u30c6\u30af\u30c1\u30e3\u3092\u57fa\u76e4\u3068\u3057\u3066\u3044\u307e\u3059\u3002<\/p>\n
    GridDB\u304b\u3089\u306e\u30c7\u30fc\u30bf\u53d6\u5f97<\/h3>\n
    \u96fb\u529b\u751f\u7523\u306e\u4e88\u6e2c\u3092\u884c\u3046\u305f\u3081\u3001\u307e\u305aGridDB\u306b\u4fdd\u5b58\u3057\u305f\u30c7\u30fc\u30bf\u3092\u53d6\u5f97\u3059\u308b\u5fc5\u8981\u304c\u3042\u308a\u307e\u3059\u3002\u3053\u308c\u306b\u306f\u3001\u53d6\u5f97\u3057\u305f\u3044\u30b3\u30f3\u30c6\u30ca\u540d\u3092\u5f15\u6570\u306b\u6307\u5b9a\u3057\u3066get_container\uff08\uff09<\/code>\u30e1\u30bd\u30c3\u30c9\u3092\u4f7f\u7528\u3057\u307e\u3059\u3002<\/p>\n
    \u30b3\u30f3\u30c6\u30ca\u306e query\uff08\uff09<\/code> \u30e1\u30bd\u30c3\u30c9\u3092\u4f7f\u7528\u3057\u3066 SELECT *<\/code> \u30af\u30a8\u30ea\u3092\u5b9f\u884c\u3057\u307e\u3059\u3002\u6b21\u306b\u3001fetch\uff08\uff09<\/code> \u30e1\u30bd\u30c3\u30c9\u3092\u547c\u3073\u51fa\u3057\u3066\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u30aa\u30d6\u30b8\u30a7\u30af\u30c8\u3092\u53d6\u5f97\u3057\u307e\u3059\u3002\u6700\u5f8c\u306b\u3001fetch_rows\uff08\uff09<\/code> \u30e1\u30bd\u30c3\u30c9\u3092\u547c\u3073\u51fa\u3057\u3066\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u3092 pandas DataFrame \u306b\u683c\u7d0d\u3057\u307e\u3059\u3002<\/p>\n
    \n
    def retrieve_data_from_griddb(container_name):\n\n    try:\n        stock_data_container = gridstore.get_container(container_name)\n\n        # Query all data from the container\n        query = stock_data_container.query(\"select *\")\n        rs = query.fetch()  # Adjust the number based on your data size\n\n        data = rs.fetch_rows()\n        data .set_index(\"Timestamp\", inplace=True)\n        return data\n\n    except griddb.GSException as e:\n        print(f\"Error retrieving data from GridDB: {e.get_message()}\")\n        return None\n\n\nelectric_production_data = retrieve_data_from_griddb(\"Electricity_Production\")\nelectric_production_data.head()<\/code><\/pre>\n<\/div>\n
    \u51fa\u529b:<\/strong><\/p>\n
    \"\"<\/a><\/p>\n
    Amazon Chronos \u30e2\u30c7\u30eb\u3092\u7528\u3044\u305f\u96fb\u529b\u751f\u7523\u306e\u4e88\u6e2c<\/h3>\n
    Amazon Chronos \u30e2\u30c7\u30eb\u306f Hugging Face<\/a>\u3067\u7121\u6599\u3067\u5229\u7528\u53ef\u80fd\u3067\u3059\u3002\u63a8\u8ad6\u3092\u884c\u3046\u305f\u3081\u306b\u306f\u3001GitHub<\/a>.\u304b\u3089\u30e2\u30c7\u30eb\u3092\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u3059\u308b\u3060\u3051\u3067\u3059\u3002<\/p>\n
    \u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u3092\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0\u30bb\u30c3\u30c8\u3068\u30c6\u30b9\u30c8\u30bb\u30c3\u30c8\u306b\u5206\u5272\u3057\u307e\u3059\u3002\u6b21\u306b\u3001Amazon Chronos\u30e2\u30c7\u30eb\u3092\u4f7f\u7528\u3057\u3066\u3001\u30c6\u30b9\u30c8\u30bb\u30c3\u30c8\u306e\u6708\u3054\u3068\u306e\u96fb\u529b\u751f\u7523\u91cf\u3092\u4e88\u6e2c\u3057\u307e\u3059\u3002\u6700\u5f8c\u306b\u3001\u4e88\u6e2c\u3057\u305f\u96fb\u529b\u751f\u7523\u91cf\u3068\u5b9f\u969b\u306e\u751f\u7523\u91cf\u3092\u6bd4\u8f03\u3057\u3066\u3001\u30e2\u30c7\u30eb\u306e\u6027\u80fd\u3092\u8a55\u4fa1\u3057\u307e\u3059\u3002<\/p>\n
    \u4ee5\u4e0b\u306e\u30b9\u30af\u30ea\u30d7\u30c8\u306f\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u3092\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0\u30bb\u30c3\u30c8\u3068\u30c6\u30b9\u30c8\u30bb\u30c3\u30c8\u306b\u5206\u5272\u3057\u307e\u3059\u3002\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306b\u306f\u5408\u8a08397\u4ef6\u306e\u30ec\u30b3\u30fc\u30c9\u304c\u542b\u307e\u308c\u3066\u3044\u307e\u3059\u3002\u30c6\u30b9\u30c8\u7528\u306b\u6700\u5f8c\u306e47\u4ef6\u3092\u4f7f\u7528\u3057\u307e\u3059\u3002<\/p>\n
    \n
    # Define the test size and calculate the split index\ntest_size = 47\nsplit_index = len(electric_production_data) - test_size\n\n# Check if the data length is shorter than the test size\nif split_index < 0:\n    train_production = pd.Series(dtype=float)\n    test_production = electric_production_data['Production']\nelse:\n    # Splitting the Production column into training and test sets\n    train_production = electric_production_data['Production'].iloc[:split_index]\n    test_production = electric_production_data['Production'].iloc[split_index:]\n\n# Display the results\nprint(\"Training Set:\")\nprint(train_production.shape)\nprint(\"nTest Set:\")\nprint(test_production.shape)<\/code><\/pre>\n<\/div>\n
    \u51fa\u529b:<\/strong><\/p>\n
    \n
    Training Set:\n(350,)\n\nTest Set:\n(47,)<\/code><\/pre>\n<\/div>\n
    \u6b21\u306b\u3001ChronosPipeline.from_pretrained\uff08\uff09<\/code> \u30e1\u30bd\u30c3\u30c9\u3092\u4f7f\u7528\u3057\u3066\u3001\u4e8b\u524d\u5b66\u7fd2\u6e08\u307f\u306e Chronos t5 large \u30e2\u30c7\u30eb\u3092\u30a4\u30f3\u30dd\u30fc\u30c8\u3057\u307e\u3059\u3002<\/p>\n
    \n
    pipeline = ChronosPipeline.from_pretrained(\n  \"amazon\/chronos-t5-large\",\n  device_map=\"cuda\",\n  torch_dtype=torch.bfloat16,\n)<\/code><\/pre>\n<\/div>\n
    Chronos\u30e2\u30c7\u30eb\u306f\u3001\u30c7\u30fc\u30bf\u304cTorch\u30c6\u30f3\u30bd\u30eb\u5f62\u5f0f\u3067\u683c\u7d0d\u3055\u308c\u3066\u3044\u308b\u3053\u3068\u3092\u60f3\u5b9a\u3057\u3066\u3044\u307e\u3059\u3002\u4ee5\u4e0b\u306e\u30b9\u30af\u30ea\u30d7\u30c8\u306f\u3001\u30c7\u30fc\u30bf\u3092Torch\u30c6\u30f3\u30bd\u30eb\u306b\u5909\u63db\u3057\u307e\u3059\u3002\u6b21\u306b\u3001\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0\u30c7\u30fc\u30bf\uff08\u30b3\u30f3\u30c6\u30ad\u30b9\u30c8\uff09\u306b\u57fa\u3065\u3044\u3066\u3001\u6b21\u306e47\u30f6\u6708\u5206\u306e\u96fb\u529b\u751f\u7523\u3092\u4e88\u6e2c\u3059\u308b\u305f\u3081\u306b\u3001pipeline.predict\uff08\uff09<\/code>\u30e1\u30bd\u30c3\u30c9\u3092\u4f7f\u7528\u3057\u307e\u3059\u3002<\/p>\n
    \u4e88\u6e2c\u7d50\u679c\u30923\u3064\u306e\u56db\u5206\u4f4d\u6570\uff080.1\u30010.5\u30010.9\uff09\u306b\u5206\u5272\u3057\u307e\u3059\u3002<\/p>\n
    \n
    context = torch.tensor(train_production)\nprediction_length = test_size\nforecast = pipeline.predict(context, prediction_length)\nlow, median, high = np.quantile(forecast[0].numpy(), [0.1, 0.5, 0.9], axis=0)<\/code><\/pre>\n<\/div>\n
    \u6b21\u306b\u3001\u30e2\u30c7\u30eb\u306e\u6027\u80fd\u3092\u8a55\u4fa1\u3057\u307e\u3059\u3002<\/p>\n
    \u30e2\u30c7\u30eb\u306e\u6027\u80fd\u8a55\u4fa1<\/h3>\n
    \u4e88\u6e2c\u5024\u306e\u4e2d\u592e\u5024\u3092\u30c6\u30b9\u30c8\u5024\u306b\u5bfe\u3057\u3066\u30d7\u30ed\u30c3\u30c8\u3057\u307e\u3059\u3002\u305d\u306e\u305f\u3081\u3001\u4e88\u6e2c\u5024\u3092\u542b\u3080pandas DataFrame\u3092\u4f5c\u6210\u3057\u307e\u3059\u3002<\/p>\n
    \u4ee5\u4e0b\u306e\u30b9\u30af\u30ea\u30d7\u30c8\u306f\u3001\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0\u30bb\u30c3\u30c8\u3001\u30c6\u30b9\u30c8\u30bb\u30c3\u30c8\u3001\u304a\u3088\u3073\u4e88\u6e2c\u5024\u3092\u30d7\u30ed\u30c3\u30c8\u3057\u307e\u3059\u3002<\/p>\n
    \n
    \ntest_production.index = pd.to_datetime(test_production.index)\nmedian_forecast = pd.Series(median, index=test_production.index, name=\"Median Forecast\")\n\nplt.figure(figsize=(12, 6))\nplt.plot(train_production.index, train_production, color='blue', label=\"Training Set\", linestyle=\"-\")\nplt.plot(test_production.index, test_production, color='green', linestyle=\"--\", label=\"Test Set\")\nplt.plot(median_forecast.index, median_forecast, color='red', linestyle=\":\", label=\"Median Forecast\")\n\n# Vertical line to mark the start of the test set\nplt.axvline(x=test_production.index[0], color='black', linestyle=\"--\", label=\"Test Set Start\")\n\nplt.xlabel(\"Timestamp\")\nplt.ylabel(\"Production\")\nplt.title(\"Production - Training, Test, and Predictions (Median Forecast)\")\nplt.legend()\nplt.show() <\/code><\/pre>\n<\/div>\n
    \u51fa\u529b:<\/strong><\/p>\n
    \"\"<\/a><\/p>\n
    \u4e0a\u8a18\u306e\u51fa\u529b\u7d50\u679c\u304b\u3089\u3001\u5f53\u793e\u306e\u30e2\u30c7\u30eb\u304c\u826f\u597d\u306a\u6027\u80fd\u3092\u793a\u3057\u3001\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306e\u50be\u5411\u3092\u9069\u5207\u306b\u6355\u6349\u3067\u304d\u3066\u3044\u308b\u3053\u3068\u304c\u308f\u304b\u308a\u307e\u3059\u3002\u4e88\u6e2c\u5024\u306f\u30c6\u30b9\u30c8\u30bb\u30c3\u30c8\u306e\u5024\u306b\u8fd1\u63a5\u3057\u3066\u3044\u307e\u3059\u3002<\/p>\n
    \u6b21\u306b\u3001\u30c6\u30b9\u30c8\u5024\u306e\u307f\u3092\u4e2d\u592e\u5024\u306e\u4e88\u6e2c\u5024\u306880%\u4e88\u6e2c\u533a\u9593\u306b\u5bfe\u3057\u3066\u30d7\u30ed\u30c3\u30c8\u3057\u307e\u3059\u3002<\/p>\n
    \n
    median_forecast = pd.Series(median, index=test_production.index, name=\"Median Forecast\")\nlower_bound = pd.Series(low, index=test_production.index, name=\"Lower Bound\")\nupper_bound = pd.Series(high, index=test_production.index, name=\"Upper Bound\")\n\n\nplt.figure(figsize=(12, 6))\n\nplt.plot(test_production.index, test_production, color='green', linestyle=\":\", label=\"Actual Production\")\nplt.plot(median_forecast.index, median_forecast, color='red', linestyle=\":\", label=\"Median Forecast\")\n\n# Plot the 80% prediction interval as an orange shaded area\nplt.fill_between(test_production.index, lower_bound, upper_bound, color='orange', alpha=0.3, label=\"80% Prediction Interval\")\n\nplt.xlabel(\"Timestamp\")\nplt.ylabel(\"Production\")\nplt.title(\"Production - Actual vs. Forecast with 80% Prediction Interval\")\nplt.legend()\nplt.show()<\/code><\/pre>\n<\/div>\n
    \u51fa\u529b:<\/strong><\/p>\n
    \"\"<\/a><\/p>\n
    \u4e0a\u8a18\u306e\u51fa\u529b\u7d50\u679c\u304b\u3089\u3001\u4e88\u6e2c\u5024\u306e80%\u533a\u9593\u304c\u30c6\u30b9\u30c8\u30c7\u30fc\u30bf\u306e\u307b\u307c\u3059\u3079\u3066\u3092\u30ab\u30d0\u30fc\u3057\u3066\u3044\u308b\u3053\u3068\u304c\u308f\u304b\u308a\u307e\u3059\u3002\u3053\u308c\u306b\u3088\u308a\u3001\u5f53\u793e\u306e\u30e2\u30c7\u30eb\u304c\u6642\u7cfb\u5217\u30c7\u30fc\u30bf\u306e\u4e88\u6e2c\u306b\u304a\u3044\u3066\u6975\u3081\u3066\u9ad8\u3044\u6027\u80fd\u3092\u767a\u63ee\u3057\u3066\u3044\u308b\u3053\u3068\u304c\u793a\u3055\u308c\u3066\u3044\u307e\u3059\u3002<\/p>\n
    \u6700\u5f8c\u306b\u3001\u4e88\u6e2c\u7d50\u679c\u306e\u5e73\u5747\u7d76\u5bfe\u8aa4\u5dee\uff08MAE\uff09\u5024\u3092\u30d7\u30ed\u30c3\u30c8\u3057\u3001\u7d50\u679c\u3092\u5b9a\u91cf\u5316\u3057\u307e\u3059\u3002<\/p>\n
    \n
    mae = mean_absolute_error(test_production, median_forecast)\n\n# Print results\nprint(\"Average electricity production values in the training set:\", train_production.mean())\nprint(\"Mean Absolute Error (MAE):\", mae)<\/code><\/pre>\n<\/div>\n
    \u51fa\u529b:<\/strong><\/p>\n
    \u30c8\u30ec\u30fc\u30cb\u30f3\u30b0\u30bb\u30c3\u30c8\u306b\u304a\u3051\u308b\u5e73\u5747\u96fb\u529b\u751f\u6210\u5024: 86.9775362857143 \u5e73\u5747\u7d76\u5bfe\u8aa4\u5dee (MAE): 3.2303302385930803<\/p>\n
    MAE\u5024\u306f3.23\u3068\u306a\u308a\u3001\u5e73\u5747\u3057\u3066\u5f53\u793e\u306e\u30e2\u30c7\u30eb\u306e\u4e88\u6e2c\u5024\u306f\u5b9f\u969b\u306e\u30c6\u30b9\u30c8\u5024\u304b\u30893.23\u5358\u4f4d\u306e\u8aa4\u5dee\u304c\u3042\u308a\u3001\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0\u30bb\u30c3\u30c8\u306e\u5e73\u5747\u96fb\u529b\u751f\u7523\u91cf\u304b\u30893.4%\u306e\u504f\u5dee\u3092\u793a\u3057\u3066\u3044\u307e\u3059\u3002<\/p>\n
    \u7d50\u8ad6<\/h2>\n
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