Request (Timeseries)

Method

API is using POST method

Supports: ✅ Time Series Forecasting (predicting numeric values over a date range)

Request URL

https://app.graphite-note.com/api/v1/prediction/model/[model-code]

Replace [model-code] with your model’s code. Find it in the model’s Settings → ID section.

Headers

• Authorization: Bearer [token] (Get your token in Account Info inside the app.)

• Content-Type: application/json

Body

The v1 time-series API expects a structured object at data.predict_values:

Required fields

• startDate (string, YYYY-MM-DD)

• endDate (string, YYYY-MM-DD)

• sequenceID (string; use "n/a" if not applicable)

Conditional field

• daysData (array) — only when the model was trained with regressors. One object per date with:

  • date (string, YYYY-MM-DD)

  • All regressor columns used in training (e.g., promotion, price, weather, …)

If the model used regressors, you must supply every regressor for every date in the requested range via daysData.

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Example A — Without regressors

curl -L -X POST 'https://app.graphite-note.com/api/v1/prediction/model/[model-code]' \
  -H 'Authorization: Bearer [token]' \
  -H 'Content-Type: application/json' \
  -d '{
    "data": {
      "predict_values": {
        "startDate": "2025-10-01",
        "endDate":   "2025-12-01",
        "sequenceID": "Store001"
      }
    }
  }'

Typical response

{
  "data": [
    {
      "date": "2025-10-01T00:00:00.000",
      "predicted": 1225707.2645253073,
      "predicted_lower": 1190271.3575973918,
      "predicted_upper": 1257483.2005986127
    },
    {
      "date": "2025-11-01T00:00:00.000",
      "predicted": 1354638.5614777768,
      "predicted_lower": 1322609.8547555234,
      "predicted_upper": 1388121.5808224094
    },
    {
      "date": "2025-12-01T00:00:00.000",
      "predicted": 1882129.6128404615,
      "predicted_lower": 1847289.9345675157,
      "predicted_upper": 1913548.0087476152
    },
    {
      "sequenceID": "Store001"
    }
  ]
}

---

Example B — With regressors (daysData required)

curl -L -X POST 'https://app.graphite-note.com/api/v1/prediction/model/[model-code]' \
  -H 'Authorization: Bearer [token]' \
  -H 'Content-Type: application/json' \
  -d '{
    "data": {
      "predict_values": {
        "startDate": "2025-09-22",
        "endDate":   "2025-09-29",
        "sequenceID": "Alpha",
        "daysData": [
          { "date": "2025-09-22", "promotion": "A" },
          { "date": "2025-09-29", "promotion": "B" }
        ]
      }
    }
  }'

Typical response

{
  "data": [
    {
      "date": "2025-09-22T00:00:00.000",
      "predicted": 2370.4695876452001,
      "predicted_lower": 2279.2000645561998,
      "predicted_upper": 2458.3810902849
    },
    {
      "date": "2025-09-29T00:00:00.000",
      "predicted": 2694.2617946204,
      "predicted_lower": 2604.5116258851999,
      "predicted_upper": 2788.9465525872001
    },
    {
      "sequenceID": "Alpha"
    }
  ]
}

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Parameter Reference

Field	Type	Required	Description
startDate	string	Yes	Start of prediction window (YYYY-MM-DD). Must align to model frequency.
endDate	string	Yes	End of prediction window (YYYY-MM-DD). Inclusive for requested outputs.
sequenceID	string	Yes	Sequence identifier. Use "n/a" if not used by your workflow.
daysData	array	Conditional	Only for models trained with regressors. One row per date.
date	string	Conditional	Inside daysData. Date the regressor values apply to (YYYY-MM-DD).
<regressor>	any	Conditional	Inside daysData. Every regressor column used in training must be present for each date.

Response fields

• date: ISO datetime string (YYYY-MM-DDT00:00:00.000) aligned to the model frequency.

• predicted: point forecast.

• predicted_lower, predicted_upper: uncertainty bounds.

• A trailing object with { "sequenceID": "…" } echoes your request identifier.

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Validation Rules & Tips

• Dates: Use YYYY-MM-DD. Ensure the range matches model frequency (daily, weekly, monthly).

• Regressors: For regressor-trained models, provide all regressors for every date in daysData. Missing any will prevent forward predictions.

• Types: Match training schema. Numeric vs categorical must align with what the model expects.

• Range size: Extremely large ranges may be throttled. Test with a small window first.

• Echoed sequence: The API echoes your sequenceID at the end of the response array to help correlate requests.

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