Version: Next

TimeSeries LLM Forecast API Reference

Overview

A time series records how a measurement changes over time, while forecasting estimates future values based on past behavior. Time series forecasting is commonly used for future-looking decisions in finance, supply chains, energy, and other operations that depend on reliable projections.

To ease and standardize the deployment of time series models in the LLM era, we designed the TimeSeries LLM Forecast API in Kserve so you can run forecasts with transformer-based models. This API supports both univariate and multivariate time series, provides quantile forecasting, and is designed to be extensible for future enhancements. The API is compatible with standard RESTful practices and allows for flexible options and metadata handling, making it suitable for a wide range of time series applications across industries.

Overview
Quick Start
Request Schema
Response Schema
Enumerations
Examples
- Request Example
- Response Example
Best Practices
FAQ
Changelog

Quick Start

Endpoint

POST /v1/timeseries/forecast

Typical Workflow

Prepare your input time series data and specify forecasting options (e.g., horizon, quantiles).
Compose a JSON request payload according to the Request Schema.
Send a POST request to /v1/timeseries/forecast.
Parse the response for forecast results, quantiles, and usage metrics.

Request Schema

ForecastRequest Fields

Field	Type	Required	Description
model	string	Yes	Name/ID of the model to use
inputs	List[TimeSeriesInput]	Yes	List of input time series to forecast
options	ForecastOptions	Yes	Forecasting options (e.g., horizon, quantiles)
metadata	Metadata	No	Arbitrary user metadata
other_properties	any	No	Additional extensible fields

JSON Structure

{
  "model": "timesfm",
  "inputs": [...],
  "options": {...},
  "metadata": {...},        // optional
  "other_properties": ...   // optional
}

TimeSeriesInput

Field	Type	Required	Description
type	TimeSeriesType	Yes	'univariate_time_series' or 'multivariate_time_series'
name	string	Yes	Name of the time series (unique in request)
series	TimeSeries	Yes	Observed data: List[float] or List[List[float]]
frequency	Frequency	Yes	Frequency (see Frequency)
start_timestamp	string (ISO8601)	No	Start timestamp of series (for aligning output)
...extra fields	any	No	Additional extensible fields

ForecastOptions

Field	Type	Required	Description
horizon	int	Yes	Number of steps to forecast
quantiles	List[float]	No	Quantiles (e.g., [0.1, 0.5, 0.9])
...extra	any	No	Additional extensible fields

Metadata

Arbitrary user-provided metadata. No required or fixed fields.

Response Schema

ForecastResponse Fields

Field	Type	Required	Description
id	string	Yes	Unique response identifier
created_at	int (unix ts)	Yes	Time of response creation
status	Status	Yes	Overall status of the request
error	Error	No	Top-level error (if any)
model	string	Yes	The model used for forecasting
outputs	List[ForecastOutput]	Yes	Forecast results, one per input
usage	Usage	No	Token usage metrics (if relevant)
...extra	any	No	Additional extensible fields

ForecastOutput

Field	Type	Required	Description
type	string	Yes	'time_series_forecast'
id	string	Yes	Unique forecast output identifier
status	Status	Yes	Status of this forecast result
content	List[TimeSeriesForecast]	Yes	One or more time series forecasts (per input)
error	Error	No	Error if this forecast failed
...extra	any	No	Additional extensible fields

TimeSeriesForecast

Field	Type	Required	Description
type	TimeSeriesType	Yes	'univariate_time_series' or 'multivariate_time_series'
name	string	Yes	The name of the time series
mean_forecast	TimeSeries	Yes	Mean (expected) forecast values
frequency	Frequency	Yes	Frequency of the forecasted time series
start_timestamp	string (ISO8601)	Yes	Start timestamp for the forecast horizon
quantiles	Dict[str, TimeSeries]	No	Map from quantile string (e.g., "0.1") to values
...extra	any	No	Additional extensible fields

Usage

Field	Type	Required	Description
prompt_tokens	int	Yes	Number of tokens in prompt (if LLM-related)
completion_tokens	int	Yes	Number of tokens in result
total_tokens	int	Yes	Total tokens used
...extra	any	No	Additional extensible fields

Error Handling

If an error occurs, the response may contain a top-level error field, or errors may be reported per-forecast (in the corresponding output object). The structure is:

{
  "error": {
    "code": "<string>",
    "message": "<description>",
    "param": "<string>",
    "type": "<string>"
  }
}

Enumerations

Frequency

SECOND, S: second
MINUTE, T: minute
HOUR, H: hour
DAY, D: day
WEEK, W: week
MONTH, M: month
QUARTER, Q: quarter
YEAR, Y: year

Status

COMPLETED: The forecast completed successfully
ERROR: There was an error in processing
PENDING: The forecast is still running
PARTIAL: Partially completed

TimeSeriesType

univariate_time_series
multivariate_time_series

Examples

Deploy on KServe

The steps below assume you already have a Kubernetes cluster with KServe installed and that you can pull the model weights from Hugging Face (set the HUGGING_FACE_HUB_TOKEN secret if the model is gated).

apiVersion: serving.kserve.io/v1beta1
kind: InferenceService
metadata:
  name: timeseries-forecast
  namespace: kserve-demo
spec:
  predictor:
    containers:
      - name: timesfm
        image: kserve/huggingfaceserver:latest
        args:
          - "--model_id=google/timesfm-2.0-500m-pytorch"
          - "--model_name=timesfm2"
          - "--http_port=8080"
        resources:
          requests:
            cpu: "4"
            memory: "8Gi"
          limits:
            cpu: "8"
            memory: "16Gi"
            nvidia.com/gpu: "1"

Request Example

curl -X POST "${SERVICE_URL}/v1/timeseries/forecast" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "timesfm2",
    "inputs": [
      {
        "type": "univariate_time_series",
        "name": "stock_price",
        "series": [120, 122, 125, 127, 130, 133, 135],
        "frequency": "D",
        "start_timestamp": "2025-06-05T13:10:00Z"
      },
      {
        "type": "univariate_time_series",
        "name": "humidity",
        "series": [33, 34, 35, 36, 37],
        "frequency": "H",
        "start_timestamp": "2025-06-05T13:10:00Z"
      }
    ],
    "options": {
      "horizon": 4,
      "quantiles": [0.1, 0.5, 0.9],
      "other_options": "value"
    },
    "metadata": {
      "request_id": "user_defined_request_id",
      "other_data": "value"
    },
    "other_properties": "value"
  }'

Response Example

{
  "id": "resp_67ccd2bed1ec8190b14f964abc0542670bb6a6b452d3795b",
  "created_at": 1741476542,
  "status": "completed",
  "error": null,
  "model": "timesfm2",
  "outputs": [
    {
      "type": "time_series_forecast",
      "id": "ts_67ccd2bf17f0819081ff3bb2cf6508e60bb6a6b452d3795b",
      "status": "completed",
      "content": [
        {
          "type": "univariate_time_series",
          "name": "stock_price",
          "mean_forecast": [138, 141, 144, 147],
          "frequency": "D",
          "start_timestamp": "2025-06-12T13:10:00Z",
          "quantiles": {
            "0.1": [135, 138, 140, 143],
            "0.5": [138, 141, 144, 147],
            "0.9": [142, 145, 148, 151]
          }
        }
      ]
    },
    {
      "type": "time_series_forecast",
      "id": "ts_67ccd2bsdf9q3wadfk439jngjmaphng0oswa34nm8we0ejrf",
      "status": "completed",
      "content": [
        {
          "type": "univariate_time_series",
          "name": "humidity",
          "mean_forecast": [39, 40, 41, 42],
          "frequency": "H",
          "start_timestamp": "2025-06-05T18:10:00Z",
          "quantiles": {
            "0.1": [37, 38, 39, 40],
            "0.5": [39, 40, 41, 42],
            "0.9": [41, 42, 43, 44]
          }
        }
      ]
    }
  ],
  "usage": {
    "prompt_tokens": 4,
    "completion_tokens": 2,
    "total_tokens": 6
  },
  "other_properties": "value"
}

Best Practices

Always include start_timestamp for correct alignment of output forecast horizon with input data.
Quantiles are returned as string keys (e.g., "0.1").
Make use of metadata to pass request-tracking info (e.g., request_id).
Check both top-level and per-output status and error fields to handle errors robustly.

FAQ

Q: What happens if quantiles are not specified? A: Only mean forecast will be returned.

Q: How is the forecast start_timestamp determined? A: It is typically input.start_timestamp + options.horizon intervals, but the exact logic depends on the model and frequency.

Q: How are multivariate series represented? A: As List[List[float]], with the first dimension for time and the second for variables.

Q: Are additional fields allowed? A: Yes, both request and response models allow arbitrary extra fields for forward compatibility.

Changelog

2025-07-24: Initial version of API documentation created.
2025-10-20: Added Kubernetes deployment example.

Overview​

Table of Contents​

Quick Start​

Endpoint​

Typical Workflow​

Request Schema​

ForecastRequest Fields​

JSON Structure​

TimeSeriesInput​

ForecastOptions​

Metadata​

Response Schema​

ForecastResponse Fields​

ForecastOutput​

TimeSeriesForecast​

Usage​

Error Handling​

Enumerations​

Frequency​

Status​

TimeSeriesType​

Examples​

Deploy on KServe​

Request Example​

Response Example​

Best Practices​

FAQ​

Changelog​