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POST /forecast

This endpoint allows you to get a time series forecast from the API.

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Base URL

https://api.nolano.ai/forecast

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Authentication

An API key must be provided in the request headers using the Authorization header. 
Authorization: your_api_key_here

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Headers

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X-Model-Id
string
The ID of the model you want to use for the forecast. If not provided, the default model will be used.

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Available Models

The API provides four different forecasting models, each optimized for different use cases:

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Request Body

The request must be a JSON object with the following structure.
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series
array
required
An array containing one or more time series objects. For now, only one is supported.
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forecast_horizon
integer
required
The number of future periods you want to predict.
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data_frequency
string
required
The frequency of your input timestamps. Must be one of: “Seconds”, “Minutes”, “Hours”, “Daily”, “Weekly”, “Monthly”, “Quarterly”, “Yearly”.
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forecast_frequency
string
required
The desired frequency of the forecast output. Currently, this must match data_frequency.
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confidence
float
default:"0.95"
The desired confidence level for the upper and lower forecast bounds. Must be between 0 and 1 (e.g., 0.95 for 95%).

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Example Request

curl --location 'https://api.nolano.ai/forecast' \
--header 'Content-Type: application/json' \
--header 'Authorization: your_api_key_here' \
--header 'X-Model-Id: forecast-model-1' \
--data '{
  "series": [
    {
      "timestamps": ["2023-01-01T00:00:00", "2023-01-02T00:00:00"],
      "values": [100, 102]
    }
  ],
  "forecast_horizon": 24,
  "data_frequency": "Daily",
  "forecast_frequency": "Daily",
  "confidence": 0.95
}'

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Response

On a successful request, the API will return a 200 OK status with a JSON object containing the forecast.
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forecast_timestamps
array of strings
The timestamps for the forecasted values.
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lower_bound
array of numbers
The lower bound of the forecast confidence interval.
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median
array of numbers
The median forecasted values.
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upper_bound
array of numbers
The upper bound of the forecast confidence interval.

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Example Response

{
    "forecast_timestamps": ["2024-01-01T00:00:00", "2024-01-02T00:00:00"],
    "lower_bound": [145.5, 146.5],
    "median": [150.0, 151.0],
    "upper_bound": [154.5, 155.5]
}