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{
  "$schema": "https://mintlify.com/schema.json",
  "name": "Nixtla",
  "logo": {
    "light": "/light.png",
    "dark": "/dark.png"
  },
  "favicon": "/favicon.svg",
  "colors": {
    "primary": "#0E0E0E",
    "light": "#FAFAFA",
    "dark": "#0E0E0E",
    "anchors": {
      "from": "#2AD0CA",
      "to": "#0E00F8"
    }
  },
  "topbarCtaButton": {
    "type": "github",
    "url": "https://github.com/Nixtla/nixtla"
  },
  "navigation": [
    {
      "group": "Getting Started",
      "pages": [
        "docs/getting-started/1_introduction",
        "docs/getting-started/2_quickstart",
        "docs/getting-started/21_polars_quickstart",
        "docs/getting-started/22_azure_quickstart",
        "docs/getting-started/3_setting_up_your_api_key",
        "docs/getting-started/4_data_requirements",
        "docs/getting-started/41_pricing",
        "docs/getting-started/5_faq",
        "docs/getting-started/6_glossary",
        "docs/getting-started/7_why_timegpt"
      ]
    },
    {
      "group": "Capabilities",
      "pages": [
        {
          "group": "Forecast",
          "pages": [
            "docs/capabilities/forecast/01_quickstart",
            "docs/capabilities/forecast/02_exogenous_variables",
            "docs/capabilities/forecast/03_holidays_special_dates",
            "docs/capabilities/forecast/04_categorical_variables",
            "docs/capabilities/forecast/05_longhorizon",
            "docs/capabilities/forecast/06_multiple_series",
            "docs/capabilities/forecast/07_finetuning",
            "docs/capabilities/forecast/08_custom_loss_function",
            "docs/capabilities/forecast/09_cross_validation",
            "docs/capabilities/forecast/10_prediction_intervals",
            "docs/capabilities/forecast/11_irregular_timestamps"
          ]
        },
        {
          "group": "Historical Anomaly Detection",
          "pages": [
            "docs/capabilities/historical-anomaly-detection/01_quickstart",
            "docs/capabilities/historical-anomaly-detection/02_anomaly_exogenous",
            "docs/capabilities/historical-anomaly-detection/03_anomaly_detection_date_features",
            "docs/capabilities/historical-anomaly-detection/04_confidence_levels"
          ]
        },
        {
          "group": "Online Anomaly Detection",
          "pages": [
            "docs/capabilities/online-anomaly-detection/01_quickstart",
            "docs/capabilities/online-anomaly-detection/02_adjusting_detection_process",
            "docs/capabilities/online-anomaly-detection/03_univariate_vs_multivariate_anomaly_detection"
          ]
        }
      ]
    },
    {
      "group": "Deployment",
      "pages": [
        "docs/deployment/2_azure_ai"
      ]
    },
    {
      "group": "Tutorials",
      "pages": [
        "docs/tutorials/20_anomaly_detection",
        {
          "group": "Exogenous variables",
          "pages": [
            "docs/tutorials/01_exogenous_variables",
            "docs/tutorials/02_holidays",
            "docs/tutorials/03_categorical_variables",
            "docs/tutorials/21_shap_values"
          ]
        },
        {
          "group": "Training",
          "pages": [
            "docs/tutorials/04_longhorizon",
            "docs/tutorials/05_multiple_series"
          ]
        },
        {
          "group": "Fine-tuning",
          "pages": [
            "docs/tutorials/06_finetuning",
            "docs/tutorials/061_reusing_finetuned_models",
            "docs/tutorials/07_loss_function_finetuning",
            "docs/tutorials/23_finetune_depth_finetuning"
          ]
        },
        {
          "group": "Validation",
          "pages": [
            "docs/tutorials/08_cross_validation",
            "docs/tutorials/09_historical_forecast"
          ]
        },
        {
          "group": "Uncertainty quantification",
          "pages": [
            "docs/tutorials/10_uncertainty_quantification_with_quantile_forecasts",
            "docs/tutorials/11_uncertainty_quantification_with_prediction_intervals"
          ]
        },
        {
          "group": "Special Topics",
          "pages": [
            "docs/tutorials/13_bounded_forecasts",
            "docs/tutorials/14_hierarchical_forecasting",
            "docs/tutorials/23_temporalhierarchical",
            "docs/tutorials/15_missing_values",
            "docs/tutorials/22_how_to_improve_forecast_accuracy"
          ]
        },
        {
          "group": "Computing at scale",
          "pages": [
            "docs/tutorials/16_computing_at_scale",
            "docs/tutorials/17_computing_at_scale_spark_distributed",
            "docs/tutorials/18_computing_at_scale_dask_distributed",
            "docs/tutorials/19_computing_at_scale_ray_distributed"
          ]
        }
      ]
    },
    {
      "group": "Use cases",
      "pages": [
        "docs/use-cases/1_forecasting_web_traffic",
        "docs/use-cases/2_bitcoin_price_prediction",
        "docs/use-cases/3_electricity_demand",
        "docs/use-cases/4_intermittent_demand",
        "docs/use-cases/5_what_if_pricing_scenarios_in_retail"
      ]
    },
    {
      "group": "API Reference",
      "pages": [
        "nixtla_client",
        "date_features",
        "docs/reference/03_excel_addin",
        "docs/reference/04_nixtlar"
      ]
    }
  ]
}