Use the IBM Granite time series foundation models and forecast API to forecast future values
最終更新: 2025年3月20日
時系列予測APIとwatsonx.aiから入手可能なIBM Granite時系列モデルを使用して、過去のデータに基づいて将来の値を予測します。
開発方法
時基盤モデルから予測値を生成するには、以下のプログラミング方法を使用します
概要
時系列予測APIを使用して、過去のデータ観測値をゼロショット推論で将来の値を予測できる基盤モデルに渡します。 例えば、この方法を使用して、以下のタイプのデータの時間ベースの観測値から将来の値を予測することができます
- 株価と取引量
- 心電図（EKG）データまたは睡眠ポリグラフ（PSG）記録
- 温度データと地震計データ
- ネットワーク・パフォーマンス測定
対応基礎モデル
以下のモデル ID を持つIBM Granite時系列基盤モデルは、watsonx.ai:の予測に使用できます：
ibm/granite-ttm-512-96-r2ibm/granite-ttm-1024-96-r2ibm/granite-ttm-1536-96-r2
Granite の時系列モデルは、 IBM Researchによる多変量時系列予測用のコンパクトな事前学習済みモデルであり、Tiny Time Mixers（TTM）としても知られています。
Granite の時系列モデルは、分単位または時間単位のデータポイントと最もよく機能し、デフォルトではチャンネルごとに96のデータポイントを持つ予測データセットを生成します。 最良の結果を得るためには、利用可能なデータに基づいて最も多くのデータポイントを取るモデルを使用する。
詳細は、 モデルカードをご覧ください。
使用可能な時系列モデルのリストをプログラムで取得するには、watsonx.aias a service API のList the available foundation modelsメソッドを使用します。 利用可能な時系列モデルのみを返すには、'filters=function_time_series_forecast
例：
curl -X GET \ '{url}/ml/v1/foundation_model_specs?version=2024-11-14&filters=function_time_series_forecast'
この例では、変数'{url}us-south.ml.cloud.ibm.com
Python
watsonx.ai Pythonライブラリを使えば、時系列モデルを使ってデータを予測するコードを書くことができる。 詳細は、以下を参照してください。
詳しくは、 エネルギー需要予測のための watsonx.ai 時系列予測APIの使用をご覧ください。
Python ノートブックのサンプル
Node.js
watsonx.ai Node.js SDK を使用して時系列モデルを使用してデータを予測するコードを書くことができます。 詳しくは、以下のリソースを参照してください。
さらに詳しく知りたい方は、 コード例をご覧ください。
REST API
watsonx.ai REST API の時系列予測メソッドを使用して、過去のデータに基づいて将来の値を予測します。
始める前に、必要なデータを確認し、サンプルリクエストから詳細を学んでください。
API リファレンス
詳細はAPIリファレンス・ドキュメントを参照。
REST API データ要件
時系列とは、時間をかけて収集されたデータポイントの集まりである。
APIリクエストペイロードに含めるデータを記述するには、以下のスキーマを使用します
"schema": { "timestamp_column": "{date-column-name}", "id_columns": [ "{series-name}", "{series-name}" ] "freq":"{time-interval}", "target_columns": [ "{channel-name}", "{channel-name}", "{channel-name}" ] },
Granite時系列モデルを使用して将来の値を予測するには、分析のために提出するデータが以下の要件を満たしている必要があります：
記録されるデータは、気温や株価などの数値でなければならない。
あなたのデータには、モデルが予測を行うのに十分な過去のデータポイントが含まれていなければなりません。 各時系列基盤モデルは、リクエスト内のチャネルごとに必要なデータ ポイントの最小数を指定します。 必要数以上を指定した場合、モデルはモデル要件までの最新のデータポイントを使用し、残りは無視する。
リクエストボディに含まれる
dateid_columnstarget_columnsnullこのサービスでは、ほとんどの一般的な日付および時刻のフォーマットが受け入れられますが、ISO 8601 フォーマット（
2024-11-12T15:06:352024-11-12T15:06:35+0000一定の頻度でデータをサンプリングする。 例えば、データは1分、1時間、1日単位で観測できる。 タイムスタンプが一様でなくてもエラーは発生しませんが、結果の質は悪くなるかもしれません。 生成された予測データは、「
freqたとえば、頻度を1日（
"freq":"1D""date": [ "2024-11-15T15:06:35", "2024-11-16T15:06:35", ... ]頻度が5分の場合（
"freq":"5min""date": [ "2024-11-15T15:06:35", "2024-11-15T15:11:35", ... ]頻度パラメータにおける日付と時刻の省略形のサポート対象の値については、pandasライブラリのドキュメントの 「期間の別名」 を参照してください。
分析のために複数のソースからデータポイントを含める場合、各データポイントは、それがどのシリーズ、または観測値の集合に属するかを表す固有の識別子を提供しなければなりません。
例えば、ショッピングの傾向を分析する場合、ホリデーシーズン中の複数店舗の売上高に興味を持つかもしれない。 次の表は、AとBの2つのシリーズを示しています。 シリーズAはロケーションAの1日あたりの売上を示し、シリーズBはロケーションBの1日あたりの売上を示しています。
表 1. サンプル販売データ 日付 シリーズID 売上合計（米ドル） 7 2023年12月 A $24,988 7 2023年12月 B $63,788 2023年12月8日 A $41,855 2023年12月8日 B $105,678 ... ... ... 表は2日間のオブザベーションを示していますが、使用するモデルによって、リクエストには512から1,536のデータポイントを含める必要があります。 テーブルのデータがAPIに送信されると、以下のようにフォーマットされる：
... "data": { "date": [ "2024-12-07T00:00:00", "2024-12-07T00:00:00", "2024-12-08T00:00:00", "2024-12-08T00:00:00", ... ], "ids": [ "A", "B", "A", "B", ... ], "sales-usd": [ 24988, 63788, 41855, 105678, ... ] }多変量データポイント、つまり同じ時間間隔の間に異なる要因を測定するデータポイントを使用することで予測することができます。
例えば、ホリデーシーズン中のある店舗の売上と返品の両方に興味があるかもしれない。 次の表は、2つのチャンネル、または記録されたデータの異なる次元や側面からのいくつかのエントリーを示しています。 一方のチャンネルには1日あたりの売上高が表示され、もう一方のチャンネルには1日あたりの返品数が表示されます。
表 2. 売上と返品データのサンプル 日付 売上高（米ドル） リターン（米ドル） 7 2023年12月 $63,788 $14,788 2023年12月8日 $105,678 $25,678 ... ... ... 表は2日間のオブザベーションを示していますが、使用するモデルによって、リクエストには512から1,536のデータポイントを含める必要があります。 id_columnフィールドは省略されています。なぜなら、データは同じ系列、つまり単一の店舗からの観測値の集合から取得されているからです。 テーブルのデータがAPIに送信されると、以下のようにフォーマットされる：
... "data": { "date": [ "2024-12-07T00:00:00", "2024-12-07T00:00:00", "2024-12-08T00:00:00", "2024-12-08T00:00:00", ... ], "sales": [ 63788, 105678, ... ], "returns": [ 14788, 25678 ... ] }複数の観測系列から得られる多変量データポイントを使用して予測することができます。 例えば、店舗Aと店舗Bの両方の売上と返品を予測するために、前の2つのシナリオを効果的に組み合わせることができます。
リクエスト内のデータは、以下のようなものになるかもしれません
"data": { "date": [ "2024-12-07T00:00:00", "2024-12-07T00:00:00", "2024-12-08T00:00:00", "2024-12-08T00:00:00", ... ], "ids": [ "A", "B", "A", "B", ... ], "sales-usd": [ 24988, // sales for store A on 7 December 2024 63788, // sales for store B on 7 December 2024 41855, 105678, ... ], "returns": [ 3997, // returns for store A on 7 December 2024 14788, // returns for store B on 7 December 2024 5768, 25678, ... ]
課金情報
基盤モデル予測の料金は、以下の式で計算されます
- 入力計算：
context length x number of series x number of channels - 出力計算：
prediction length x number of series x number of channels
これらの方程式は、以下の値を使用します
コンテクストの長さとは、 基盤モデルが予測を行う際に使用する入力値として、過去のデータポイントの数を指します。
例えば、コンテキストの長さが
1536シリーズとは、時系列で順次行われた観測の集まりである。
例えば、多くの企業の株価を比較する場合、各企業の観察された株価の履歴は別々の系列となります。 測定対象のシリーズのセットは、APIリクエスト本文の
ID_columnチャンネルとは、時系列データセット内の特定の機能または変数です。 多変量時系列データでは、各チャンネルは記録されたデータの異なる次元または側面を表します。
例えば、株価データセットでは、チャネルには始値、終値、出来高、移動平均などが含まれる可能性があります。 各測定変数は、個別のチャンネルを表します。 測定対象のチャンネルは、APIリクエスト本文の
target_columns予測長とは、モデルが予測する将来のデータポイントの数を指します。 要求された予測長が
96例えば、このモデルは今後96日間の株価を予測します。 予測の長さは、APIリクエストに
prediction_length
入力および出力データポイントの価格設定とコストの計算方法の詳細については、 ジェネレーティブAI 資産の請求詳細 をご覧ください。
REST API の例
この例では、 granite-ttm-1536-96-r2
APIリクエストには prediction_length
リクエストボディは2つのセクションに分かれている：
- スキーマ：どのデータセットを含み、どのフィールドを分析するかを指定する。
- データ：分析するデータ。
schema
"schema": { "freq": "1h", "timestamp_column": "Time (CET/CEST)", "target_columns": [ "Actual Total Load [MW]" ] },
id_columnsid_columnsid_columns
data
| 日時（日付） | 総エネルギー需要（MW） |
|---|---|
| 2024-09-30T00:00:00 | 4941 |
| 2024-09-30T01:00:00 | 4640 |
| 2024-09-30T02:00:00 | 4443 |
granite-ttm-1536-96-r2 モデルでは、モデルが将来の値を予測するのに十分なデータがあることを保証するために、リクエストにはチャンネルごとに少なくとも1,536のデータポイントが必要です。 表には3つのデータポイントが示されていますが、リクエスト本文には1,536のデータポイントが含まれています。
以下の例では、 {url}us-south.ml.cloud.ibm.com
REST API リクエストの例
curl -X POST \ 'https://{region}.ml.cloud.ibm.com/ml/v1/time_series/forecast?version=2025-02-10' \ --header 'Accept: application/json' \ --header 'Content-Type: application/json' \ --header 'Authorization: Bearer eyJraWQiOi...' \ --data '{ ... }'
データペイロードには、以下のJSONスニペットが含まれる：
{
"model_id": "ibm/granite-ttm-1536-96-r2",
"project_id": "51f3a990-4372-4ac3-9ddb-ed99d9b50840",
"schema": {
"freq": "1h",
"timestamp_column": "Time (CET/CEST)",
"target_columns": [
"Actual Total Load [MW]"
]
},
"data": {
"Time (CET/CEST)": [
"2024-09-30T00:00:00",
"2024-09-30T01:00:00",
"2024-09-30T02:00:00",
"2024-09-30T03:00:00",
"2024-09-30T04:00:00",
"2024-09-30T05:00:00",
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}
}
REST API レスポンスの例
リクエスト例に対して、以下の応答が返される。
{
"model_id": "ibm/granite-ttm-1536-96-r2",
"created_at": "2025-02-05T14:00:53.597Z",
"results": [
{
"Actual Total Load [MW]": [
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],
"Time (CET/CEST)": [
"2024-12-02T23:00:00",
"2024-12-03T00:00:00",
"2024-12-03T01:00:00",
"2024-12-03T02:00:00",
"2024-12-03T03:00:00",
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"2024-12-03T06:00:00",
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"2024-12-03T08:00:00",
"2024-12-03T09:00:00",
"2024-12-03T10:00:00",
"2024-12-03T11:00:00",
"2024-12-03T12:00:00",
"2024-12-03T13:00:00",
"2024-12-03T14:00:00",
"2024-12-03T15:00:00",
"2024-12-03T16:00:00",
"2024-12-03T17:00:00",
"2024-12-03T18:00:00",
"2024-12-03T19:00:00",
"2024-12-03T20:00:00",
"2024-12-03T21:00:00",
"2024-12-03T22:00:00",
"2024-12-03T23:00:00",
"2024-12-04T00:00:00",
"2024-12-04T01:00:00",
"2024-12-04T02:00:00",
"2024-12-04T03:00:00",
"2024-12-04T04:00:00",
"2024-12-04T05:00:00",
"2024-12-04T06:00:00",
"2024-12-04T07:00:00",
"2024-12-04T08:00:00",
"2024-12-04T09:00:00",
"2024-12-04T10:00:00",
"2024-12-04T11:00:00",
"2024-12-04T12:00:00",
"2024-12-04T13:00:00",
"2024-12-04T14:00:00",
"2024-12-04T15:00:00",
"2024-12-04T16:00:00",
"2024-12-04T17:00:00",
"2024-12-04T18:00:00",
"2024-12-04T19:00:00",
"2024-12-04T20:00:00",
"2024-12-04T21:00:00",
"2024-12-04T22:00:00",
"2024-12-04T23:00:00",
"2024-12-05T00:00:00",
"2024-12-05T01:00:00",
"2024-12-05T02:00:00",
"2024-12-05T03:00:00",
"2024-12-05T04:00:00",
"2024-12-05T05:00:00",
"2024-12-05T06:00:00",
"2024-12-05T07:00:00",
"2024-12-05T08:00:00",
"2024-12-05T09:00:00",
"2024-12-05T10:00:00",
"2024-12-05T11:00:00",
"2024-12-05T12:00:00",
"2024-12-05T13:00:00",
"2024-12-05T14:00:00",
"2024-12-05T15:00:00",
"2024-12-05T16:00:00",
"2024-12-05T17:00:00",
"2024-12-05T18:00:00",
"2024-12-05T19:00:00",
"2024-12-05T20:00:00",
"2024-12-05T21:00:00",
"2024-12-05T22:00:00",
"2024-12-05T23:00:00",
"2024-12-06T00:00:00",
"2024-12-06T01:00:00",
"2024-12-06T02:00:00",
"2024-12-06T03:00:00",
"2024-12-06T04:00:00",
"2024-12-06T05:00:00",
"2024-12-06T06:00:00",
"2024-12-06T07:00:00",
"2024-12-06T08:00:00",
"2024-12-06T09:00:00",
"2024-12-06T10:00:00",
"2024-12-06T11:00:00",
"2024-12-06T12:00:00",
"2024-12-06T13:00:00",
"2024-12-06T14:00:00",
"2024-12-06T15:00:00",
"2024-12-06T16:00:00",
"2024-12-06T17:00:00",
"2024-12-06T18:00:00",
"2024-12-06T19:00:00",
"2024-12-06T20:00:00",
"2024-12-06T21:00:00",
"2024-12-06T22:00:00"
]
}
],
"input_data_points": 1536,
"output_data_points": 96
}
REST API の例の結果
時系列モデルは、今後96時間におけるポルトガルのエネルギー需要の将来値を予測した。
次のスクリーンショットのグラフは、同じ期間における実際のエネルギー需要と予測されたエネルギー需要を比較したものです。
詳細情報
親トピック 生成的AIソリューションのコーディング
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