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Data Format Requirements

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Time Series Structure

Your time series data must follow this JSON structure: 
{
  "series": [
    {
      "timestamps": ["2023-01-01T00:00:00", "2023-01-02T00:00:00", "2023-01-03T00:00:00"],
      "values": [100, 102, 98]
    }
  ],
  "forecast_horizon": 7,
  "data_frequency": "Daily",
  "forecast_frequency": "Daily",
  "confidence": 0.95
}

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Timestamp Format

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Data Quality Requirements

Minimum Data Points

Daily/Weekly: At least 30 data points
Monthly/Quarterly: At least 12 data points
Yearly: At least 5 data points
Hourly/Minutes/Seconds: At least 100 data points

Data Completeness

No missing values in timestamps or values arrays
Arrays must have equal length
Values must be numeric (integers or floats)
Timestamps must be in chronological order

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Data Preparation Best Practices

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1. Data Cleaning

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2. Seasonality Considerations

Daily Seasonality

For daily data, consider day-of-week patterns. Include at least 4 weeks of data to capture weekly seasonality.

Monthly Seasonality

For monthly data, include at least 2 years of data to capture annual seasonality patterns.

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3. Data Granularity

Choose the right frequency: Use the highest frequency that makes sense for your use case. Higher frequency data can capture more patterns but requires more data points.

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Setup Considerations

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API Configuration

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Common Data Patterns

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Example: E-commerce Sales Data

{
  "series": [
    {
      "timestamps": [
        "2023-01-01T00:00:00", "2023-01-02T00:00:00", "2023-01-03T00:00:00",
        "2023-01-04T00:00:00", "2023-01-05T00:00:00", "2023-01-06T00:00:00",
        "2023-01-07T00:00:00", "2023-01-08T00:00:00", "2023-01-09T00:00:00",
        "2023-01-10T00:00:00"
      ],
      "values": [1200, 1350, 1100, 1400, 1600, 1800, 2200, 1500, 1300, 1400]
    }
  ],
  "forecast_horizon": 14,
  "data_frequency": "Daily",
  "forecast_frequency": "Daily",
  "confidence": 0.95
}

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Example: Website Traffic (Hourly)

{
  "series": [
    {
      "timestamps": [
        "2023-01-01T00:00:00", "2023-01-01T01:00:00", "2023-01-01T02:00:00",
        "2023-01-01T03:00:00", "2023-01-01T04:00:00", "2023-01-01T05:00:00"
      ],
      "values": [150, 120, 80, 60, 50, 70]
    }
  ],
  "forecast_horizon": 24,
  "data_frequency": "Hours",
  "forecast_frequency": "Hours",
  "confidence": 0.90
}

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Troubleshooting

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Next Steps

Make Your First Request

Follow the quickstart guide to make your first forecast request with properly formatted data.

API Reference

Explore the complete API reference for detailed parameter documentation.