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Utilizzate i modelli di fondazione delle serie temporali e le API di previsione di IBM Granite per prevedere i valori futuri
Utilizzate i modelli di fondazione delle serie temporali e le API di previsione di IBM Granite per prevedere i valori futuri
Ultimo aggiornamento: 20 mar 2025
Utilizzate le API di previsione delle serie temporali e i modelli di serie temporali IBM Granite disponibili su watsonx.ai per prevedere i valori futuri sulla base dei dati storici.
Modi per sviluppare
È possibile generare previsioni dai modelli di fondazione delle serie temporali utilizzando questi metodi di programmazione:
Panoramica
Utilizza l'API di previsione delle serie temporali per passare le osservazioni dei dati storici a un modello di base delle serie temporali in grado di prevedere i valori futuri con inferenza zero-shot. Ad esempio, è possibile utilizzare questo metodo per prevedere i valori futuri a partire dalle osservazioni temporali dei seguenti tipi di dati:
- Prezzi delle azioni e volumi di scambio
- Dati di elettrocardiogramma (ECG) o registrazioni di polisonnogramma (PSG)
- Dati di temperatura o sismografici
- Misurazioni delle prestazioni della rete
Modelli di fondazione supportati
I modelli IBM Granite time series foundation con i seguenti ID modello sono disponibili per essere utilizzati per le previsioni in watsonx.ai:
ibm/granite-ttm-512-96-r2: Richiede almeno 512 punti dati per canale nella richiesta.ibm/granite-ttm-1024-96-r2: Richiede almeno 1.024 punti dati per canale nella richiesta.ibm/granite-ttm-1536-96-r2: Richiede almeno 1.536 punti dati per canale nella richiesta.
I modelli di serie temporali Granite sono modelli compatti preaddestrati per la previsione di serie temporali multivariate di IBM Research, noti anche come Tiny Time Mixers (TTM).
I modelli di serie temporali di Granite funzionano al meglio con punti dati in intervalli di minuti o ore e generano un set di dati di previsione con 96 punti dati per canale per impostazione predefinita. Per ottenere risultati ottimali, utilizzate il modello che prende il maggior numero di punti dati in base ai dati a vostra disposizione.
Per ulteriori informazioni, consultare la scheda del modello.
Per ottenere programmaticamente un elenco dei modelli di serie temporali disponibili per l'uso, è possibile utilizzare il metodo List the available foundation models nell'API watsonx.ai as a service. Specificare il parametro 'filters=function_time_series_forecast per restituire solo i modelli di serie temporali disponibili.
Ad esempio:
curl -X GET \
'{url}/ml/v1/foundation_model_specs?version=2024-11-14&filters=function_time_series_forecast'
Nell'esempio, sostituire la variabile '{url} con il valore giusto per la propria istanza, ad esempio 'us-south.ml.cloud.ibm.com.
Python
È possibile scrivere codice che utilizza i modelli di serie temporali per prevedere i dati utilizzando la libreria Python watsonx.ai Per ulteriori informazioni, consultare i seguenti riferimenti:
- Interfaccia per l'inferenza di modelli di serie temporali
- Ottenere il metodo delle specifiche del modello delle serie temporali
Per saperne di più, vedere Utilizzo dell'API watsonx.ai Time Series Forecasting per prevedere la domanda di energia.
Esempio di quaderno Python
Node.js
È possibile scrivere codice che utilizzi i modelli di serie temporali per prevedere i dati utilizzando l'SDK (Software Development Kit) di watsonx.ai Node.js. Per ulteriori informazioni, consulta le seguenti risorse:
Per saperne di più, vedere l'esempio di codice.
API REST
Utilizzare il metodo di previsione delle serie temporali di watsonx.ai REST API per prevedere i valori futuri sulla base dei dati storici.
Prima di cominciare, rivedete i requisiti dei dati e imparate qualcosa dalle richieste di esempio.
- Requisiti di dati
- Informazioni di fatturazione
- Esempio di richiesta
- Esempio di risposta
- Risultati di esempio
Riferimento API
Per maggiori dettagli, consultare la documentazione di riferimento dell'API.
Requisiti dei dati dell'API REST
Una serie temporale è un insieme di punti di dati raccolti nel tempo.
Si utilizza il seguente schema per descrivere i dati da includere nel payload della richiesta API:
"schema": {
"timestamp_column": "{date-column-name}",
"id_columns": [
"{series-name}",
"{series-name}"
]
"freq":"{time-interval}",
"target_columns": [
"{channel-name}",
"{channel-name}",
"{channel-name}"
]
},
Per utilizzare i modelli di serie temporali Granite per prevedere i valori futuri, i dati inviati per l'analisi devono soddisfare i seguenti requisiti:
I dati registrati devono essere numerici, come le temperature o i prezzi delle azioni.
I dati devono includere un numero sufficiente di punti di contesto storico affinché il modello sia in grado di fare una previsione. Ogni modello di fondazione di serie temporale specifica un numero minimo richiesto di punti dati per canale nella richiesta. Se si specifica un numero superiore a quello richiesto, il modello utilizza i punti dati più recenti fino al requisito del modello e ignora gli altri.
Deve esserci un numero uguale di elementi negli array specificati per i campi
date,id_columnsetarget_columnsinclusi nel corpo della richiesta. Non è possibile saltare un punto dati o specificare "nullcome punto dati. Controllare i dati prima di inviare la richiesta.Sebbene il servizio accetti i formati di data e ora più comuni, se si specificano le date e gli orari nel formato ISO 8601 (
2024-11-12T15:06:35), si evita la confusione che può sorgere a causa delle differenze nelle convenzioni di formattazione delle date. Ad esempio, 11/12/2024 significa 12 novembre o 11 dicembre? Specificare le date nel formato ISO 8601 con l'offset del tempo universale coordinato (UTC) (2024-11-12T15:06:35+0000) per evitare duplicazioni apparenti o timestamp mancanti nei risultati di previsione generati.Campionare i dati con una frequenza uniforme. Ad esempio, i dati possono essere osservati con incrementi di 1 minuto, 1 ora o 1 giorno. Se i timestamp non sono uniformi non viene generato alcun errore, ma la qualità dei risultati potrebbe essere scarsa. I dati di previsione generati sono anche formattati con la frequenza specificata con il parametro "
freq.Ad esempio, se si specifica una frequenza di un giorno (
"freq":"1D"), le marche temporali avranno probabilmente un aspetto simile a questo:"date": [ "2024-11-15T15:06:35", "2024-11-16T15:06:35", ... ]Se la frequenza è di 5 minuti (
"freq":"5min"), ogni data e ora saranno probabilmente distanti 5 minuti l'una dall'altra."date": [ "2024-11-15T15:06:35", "2024-11-15T15:11:35", ... ]Per i valori supportati per le abbreviazioni di data e ora nel parametro frequenza, vedere Alias di periodo nella documentazione della libreria pandas.
Se si includono punti di dati provenienti da più fonti per l'analisi, ogni punto di dati deve fornire un identificatore unico per indicare a quale serie, o raccolta di osservazioni, appartiene.
Ad esempio, se state analizzando le tendenze dello shopping, potreste essere interessati ai dati di vendita di più negozi durante le festività natalizie. La tabella seguente mostra due serie, A e B. La serie A mostra le vendite giornaliere per la sede A e la serie B mostra le vendite giornaliere per la sede B.
Tabella 1. Esempio di dati di vendita Data ID serie Totale vendite (USD) 7 dicembre 2023 A $24,988 7 dicembre 2023 B $63,788 8 dicembre 2023 A $41,855 8 dicembre 2023 B $105,678 ... ... ... Anche se la tabella mostra le osservazioni di due giorni, la richiesta deve includere da 512 a 1.536 punti dati, a seconda del modello utilizzato. Quando i dati della tabella vengono inviati all'API, sono formattati come segue:
... "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, ... ] }È possibile fare previsioni utilizzando punti di dati multivariati, ossia punti di dati che misurano fattori diversi durante lo stesso intervallo di tempo.
Ad esempio, potreste essere interessati sia alle vendite che ai resi di un negozio durante le festività natalizie. La tabella seguente mostra alcune voci di due canali, o diverse dimensioni o aspetti dei dati registrati. Un canale mostra le vendite giornaliere e l'altro i ritorni giornalieri.
Tabella 2. Esempio di dati sulle vendite e sui resi Data Vendite (USD) Rendimenti (USD) 7 dicembre 2023 $63,788 $14,788 8 dicembre 2023 $105,678 $25,678 ... ... ... Anche se la tabella mostra le osservazioni di due giorni, la richiesta deve includere da 512 a 1.536 punti dati, a seconda del modello utilizzato. Il campo id_colonna viene omesso perché i dati provengono dalla stessa serie, una raccolta di osservazioni da un singolo negozio. Quando i dati della tabella vengono inviati all'API, sono formattati come segue:
... "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 ... ] }È possibile fare previsioni utilizzando punti di dati multivariati provenienti da più serie di osservazioni. Ad esempio, è possibile combinare efficacemente i due scenari precedenti per prevedere le vendite e i resi sia per il punto vendita A che per il punto vendita B.
I dati contenuti nella richiesta potrebbero avere un aspetto simile a questo:
"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, ... ]
Informazioni di fatturazione
I costi per la previsione del modello di fondazione sono calcolati utilizzando le seguenti equazioni:
- Calcolo dell'ingresso:
context length x number of series x number of channels - Calcolo dell'uscita:
prediction length x number of series x number of channels
Queste equazioni utilizzano i seguenti valori:
La lunghezza del contesto si riferisce al numero di punti di dati storici che un modello di base di serie temporali utilizza come input per fare previsioni.
Ad esempio, quando la lunghezza del contesto è
1536, il modello utilizza gli ultimi 1536 punti di dati, come i prezzi delle azioni negli ultimi 1536 giorni, per fare previsioni. La lunghezza del contesto è determinata dal modello di fondazione utilizzato per le previsioni.Una serie è un insieme di osservazioni effettuate in sequenza nel tempo.
Ad esempio, quando si confrontano i prezzi delle azioni di molte società, la storia dei prezzi delle azioni osservate per ogni società è una serie separata. L'insieme delle serie da misurare è specificato nel segmento
ID_columndel corpo della richiesta API.I canali sono caratteristiche o variabili specifiche all'interno di un set di dati di serie temporali. Nei dati delle serie temporali multivariate, ogni canale rappresenta una dimensione o un aspetto diverso dei dati registrati.
Ad esempio, in un set di dati sui prezzi delle azioni, i canali potrebbero includere il prezzo di apertura, il prezzo di chiusura, il volume e le medie mobili. Ogni variabile misurata rappresenta un canale separato. I canali da misurare sono specificati nel segmento
target_columnsdel corpo della richiesta API.La lunghezza della previsione è il numero di punti dati futuri che il modello deve prevedere. Quando la lunghezza di previsione richiesta è
96, il modello prevede i 96 punti dati successivi.Ad esempio, il modello prevede i prezzi delle azioni per i prossimi 96 giorni. La lunghezza della previsione può essere specificata includendo il parametro
prediction_lengthnella richiesta API.
Per ulteriori informazioni sulla tariffazione dei punti di dati di input e di output e su come calcolare i costi, vedere Dettagli di fatturazione per gli asset di IA generativa.
Esempio di API REST
Questo esempio utilizza il modello granite-ttm-1536-96-r2 per prevedere la domanda totale di energia del Portogallo per ora dal 30 settembre 2024 al 2 dicembre 2024. Il set di dati utilizzato nell'esempio proviene dal sito web della Rete europea dei gestori dei sistemi di trasmissione dell'energia elettrica (ENTSO-E). Per vedere i dati, andare a questa pagina del sito ENTSO-E, selezionare il Portogallo come Paese e scegliere il 29 settembre 2024 come data con il fuso orario predefinito (CET).
La richiesta API non specifica un parametro prediction_length. Pertanto, viene utilizzata la lunghezza predefinita per il modello di fondazione, il che significa che il modello di serie temporale prevede i valori futuri della domanda di energia nelle prossime 96 ore.
Il corpo della richiesta è diviso in due sezioni:
- Schema: Specifica quali set di dati saranno inclusi e quali campi analizzare.
- Dati: I dati da analizzare.
La sezione schema si presenta così:
"schema": {
"freq": "1h",
"timestamp_column": "Time (CET/CEST)",
"target_columns": [
"Actual Total Load [MW]"
]
},
Si può notare che non c'è nessun oggetto id_columns incluso nello schema. In questo esempio, vengono inviati i dati di una sola serie di dati osservati, per cui l'oggetto id_columns può essere omesso dallo schema. Tuttavia, se la richiesta include dati provenienti da più fonti, è necessario includere l'oggetto id_columns per identificare la fonte di ciascun punto dati.
La sezione data contiene informazioni sul fabbisogno energetico orario del Paese, come mostrato nella tabella seguente.
| Data e ora (data) | Domanda totale di energia (MW) |
|---|---|
| 2024-09-30T00:00:00 | 4941 |
| 2024-09-30T01:00:00 | 4640 |
| 2024-09-30T02:00:00 | 4443 |
Il modello granite-ttm-1536-96-r2 richiede almeno 1.536 punti dati per canale nella richiesta, per garantire che ci siano dati sufficienti per il modello per prevedere i valori futuri. Sebbene la tabella mostri 3 punti dati, il corpo della richiesta contiene 1.536 punti dati.
Nell'esempio seguente, sostituite la variabile {url} con il valore giusto per la vostra istanza, ad esempio us-south.ml.cloud.ibm.com. Aggiungete il vostro token al portatore e l'ID del progetto.
Esempio di richiesta API REST
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 '{
...
}'
Il payload dei dati contiene il seguente frammento JSON:
{
"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",
"2024-09-30T06:00:00",
"2024-09-30T07:00:00",
"2024-09-30T08:00:00",
"2024-09-30T09:00:00",
"2024-09-30T10:00:00",
"2024-09-30T11:00:00",
"2024-09-30T12:00:00",
"2024-09-30T13:00:00",
"2024-09-30T14:00:00",
"2024-09-30T15:00:00",
"2024-09-30T16:00:00",
"2024-09-30T17:00:00",
"2024-09-30T18:00:00",
"2024-09-30T19:00:00",
"2024-09-30T20:00:00",
"2024-09-30T21:00:00",
"2024-09-30T22:00:00",
"2024-09-30T23:00:00",
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}
}
Esempio di risposta API REST
Per la richiesta di esempio viene restituita la seguente risposta.
{
"model_id": "ibm/granite-ttm-1536-96-r2",
"created_at": "2025-02-05T14:00:53.597Z",
"results": [
{
"Actual Total Load [MW]": [
6465.83154296875,
5847.07080078125,
5351.2109375,
5033.18212890625,
4876.1064453125,
4780.388671875,
4784.177734375,
4964.26171875,
5416.59375,
6017.84619140625,
6571.35986328125,
6776.26318359375,
6817.2890625,
6812.52001953125,
6746.37744140625,
6707.9755859375,
6773.8466796875,
6847.83154296875,
6995.001953125,
7263.904296875,
7553.798828125,
7598.19580078125,
7379.0302734375,
6916.89697265625,
6339.57373046875,
5786.80908203125,
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"Time (CET/CEST)": [
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}
],
"input_data_points": 1536,
"output_data_points": 96
}
Risultati dell'esempio di API REST
Il modello delle serie temporali ha previsto i valori futuri della domanda di energia in Portogallo nelle successive 96 ore.
Il grafico nella schermata seguente mostra la domanda di energia prevista rispetto a quella effettiva nello stesso arco di tempo.
Ulteriori informazioni
Argomento principale: Codificare soluzioni di IA generativa
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