# TimeGPT Foundational model for time series forecasting and anomaly detection

## Docs

- [Key Concepts](https://nixtla-docs-broken-links-fix.mintlify.app/about/key-concepts.md): Understanding the foundations of time series forecasting with TimeGPT
- [Privacy Notice](https://nixtla-docs-broken-links-fix.mintlify.app/about/privacy-notice.md): Details on how Nixtla collects, uses, and protects your personal information.
- [Nixtla](https://nixtla-docs-broken-links-fix.mintlify.app/about/sub-categoria.md): About us
- [Terms and Conditions](https://nixtla-docs-broken-links-fix.mintlify.app/about/terms-and-conditions.md): Terms and conditions for using Nixtla Services.
- [Add Exogenous Variables](https://nixtla-docs-broken-links-fix.mintlify.app/anomaly_detection/exogenous_variables.md): Learn how to improve anomaly detection by incorporating external factors.
- [Quickstart](https://nixtla-docs-broken-links-fix.mintlify.app/anomaly_detection/historical_anomaly_detection.md): Get started with TimeGPT's historical anomaly detection capabilities.
- [Controlling the Anomaly Detection Process](https://nixtla-docs-broken-links-fix.mintlify.app/anomaly_detection/real-time/adjusting_detection.md): Learn how to refine TimeGPT's anomaly detection process by tuning parameters for improved accuracy and alignment with specific use cases.
- [Online (Real-Time) Anomaly Detection](https://nixtla-docs-broken-links-fix.mintlify.app/anomaly_detection/real-time/introduction.md): Learn how to use the detect_anomalies_online method for real-time anomaly detection in streaming time series data with TimeGPT.
- [Local vs Global Anomaly Detection](https://nixtla-docs-broken-links-fix.mintlify.app/anomaly_detection/real-time/univariate_multivariate.md): Explore the differences between single and multiple variable anomaly detection approaches.
- [Audit and Clean Data](https://nixtla-docs-broken-links-fix.mintlify.app/data_requirements/audit_clean.md): Learn how to audit and clean your data with TimeGPT.
- [Data Requirements](https://nixtla-docs-broken-links-fix.mintlify.app/data_requirements/data_requirements.md): Overview of the data format and requirements for TimeGPT forecasting.
- [Missing Values](https://nixtla-docs-broken-links-fix.mintlify.app/data_requirements/missing_values.md): Learn how to handle missing values in time series data for accurate forecasting with TimeGPT.
- [Multiple Time Series](https://nixtla-docs-broken-links-fix.mintlify.app/data_requirements/multiple_series.md): Learn how to handle missing values in time series data for accurate forecasting with TimeGPT.
- [Cross-validation Tutorial](https://nixtla-docs-broken-links-fix.mintlify.app/forecasting/evaluation/cross_validation.md): Learn how to validate time series models with rolling-window cross-validation
- [Evaluation Metrics](https://nixtla-docs-broken-links-fix.mintlify.app/forecasting/evaluation/evaluation_metrics.md): Learn to select the right evaluation metrics to measure the performance of TimeGPT.
- [Evaluation Pipeline](https://nixtla-docs-broken-links-fix.mintlify.app/forecasting/evaluation/evaluation_utilsforecast.md): Learn how to evaluate TimeGPT model performance using tools in utilforecast
- [Categorical Variables](https://nixtla-docs-broken-links-fix.mintlify.app/forecasting/exogenous-variables/categorical_features.md): Learn how to incorporate external categorical variables in your TimeGPT forecasts to improve accuracy.
- [Date/Time Features](https://nixtla-docs-broken-links-fix.mintlify.app/forecasting/exogenous-variables/date_features.md): Learn how to incorporate date/time features into your forecasts to improve performance.
- [Holidays & Special Dates](https://nixtla-docs-broken-links-fix.mintlify.app/forecasting/exogenous-variables/holiday_and_special_dates.md): Guide to using holiday calendar variables and special dates to improve forecast accuracy in time series.
- [Model Interpretability](https://nixtla-docs-broken-links-fix.mintlify.app/forecasting/exogenous-variables/interpretability_with_shap.md): Learn how to interpret model predictions using SHAP values to understand the impact of exogenous variables.
- [Numeric Variables](https://nixtla-docs-broken-links-fix.mintlify.app/forecasting/exogenous-variables/numeric_features.md): Learn how to incorporate external numeric variables to improve your forecasting accuracy.
- [Fine-tuning with a Specific Loss Function](https://nixtla-docs-broken-links-fix.mintlify.app/forecasting/fine-tuning/custom_loss.md): Learn how to fine-tune a model using specific loss functions, configure the Nixtla client, and evaluate performance improvements.
- [Controlling the Level of Fine-Tuning](https://nixtla-docs-broken-links-fix.mintlify.app/forecasting/fine-tuning/depth.md): Learn how to use the finetune_depth parameter to control the extent of fine-tuning in TimeGPT models.
- [Re-using fine-tuned models](https://nixtla-docs-broken-links-fix.mintlify.app/forecasting/fine-tuning/save_reuse_delete_finetuned_models.md): Learn how to save, fine-tune, list, and delete TimeGPT models to optimize forecasting.
- [Fine-tuning Tutorial TimeGPT](https://nixtla-docs-broken-links-fix.mintlify.app/forecasting/fine-tuning/steps.md): Adapt TimeGPT to your specific datasets for more accurate forecasts
- [Computing at Scale Tutorial](https://nixtla-docs-broken-links-fix.mintlify.app/forecasting/forecasting-at-scale/computing_at_scale.md): Learn how to use TimeGPT with distributed computing frameworks for processing large datasets.
- [Dask](https://nixtla-docs-broken-links-fix.mintlify.app/forecasting/forecasting-at-scale/dask.md): Run TimeGPT in a distributed manner using Dask for scalable forecasting.
- [Ray](https://nixtla-docs-broken-links-fix.mintlify.app/forecasting/forecasting-at-scale/ray.md): Distribute TimeGPT forecasting jobs on Ray for scalable Python workloads.
- [Spark](https://nixtla-docs-broken-links-fix.mintlify.app/forecasting/forecasting-at-scale/spark.md): Learn how to run TimeGPT in a distributed manner on Spark for scalable forecasting and cross-validation.
- [Improve Forecast Accuracy with TimeGPT](https://nixtla-docs-broken-links-fix.mintlify.app/forecasting/improve_accuracy.md): Advanced techniques to enhance TimeGPT forecast accuracy for energy and electricity.
- [Long-horizon forecasting Tutorial](https://nixtla-docs-broken-links-fix.mintlify.app/forecasting/model-version/longhorizon_model.md): Learn how to use the TimeGPT long-horizon model for forecasting far into the future with Nixtla client.
- [Uncertainty Quantification with TimeGPT](https://nixtla-docs-broken-links-fix.mintlify.app/forecasting/probabilistic/introduction.md): Learn how to generate quantile forecasts and prediction intervals to capture uncertainty in your forecasts.
- [Prediction Intervals](https://nixtla-docs-broken-links-fix.mintlify.app/forecasting/probabilistic/prediction_intervals.md): Learn how to create prediction intervals with TimeGPT
- [Quantile Forecasts](https://nixtla-docs-broken-links-fix.mintlify.app/forecasting/probabilistic/quantiles.md): Learn how to generate quantile forecasts with TimeGPT
- [Bounded Forecasts](https://nixtla-docs-broken-links-fix.mintlify.app/forecasting/special-topics/bounded_forecasts.md): Learn how to generate forecasts with upper and lower bounds to match your business constraints.
- [Hierarchical Forecasting](https://nixtla-docs-broken-links-fix.mintlify.app/forecasting/special-topics/hierarchical_forecasting.md): Learn how to use TimeGPT for hierarchical forecasting across multiple levels.
- [Irregular Timestamps](https://nixtla-docs-broken-links-fix.mintlify.app/forecasting/special-topics/irregular_timestamps.md): Learn how to work with both regular and irregular timestamps in TimeGPT for accurate forecasting.
- [Temporal Hierarchical Forecasting with TimeGPT](https://nixtla-docs-broken-links-fix.mintlify.app/forecasting/special-topics/temporal_hierarchical.md): Learn how to combine forecasts at different time frequencies to improve accuracy.
- [Quickstart](https://nixtla-docs-broken-links-fix.mintlify.app/forecasting/timegpt_quickstart.md): Learn how to use TimeGPT for accurate time series forecasting in just a few steps
- [About TimeGPT](https://nixtla-docs-broken-links-fix.mintlify.app/introduction/about_timegpt.md): Learn about TimeGPT - the foundation model for time series.
- [TimeGPT FAQ](https://nixtla-docs-broken-links-fix.mintlify.app/introduction/faq.md): Frequently asked questions about TimeGPT
- [Introduction](https://nixtla-docs-broken-links-fix.mintlify.app/introduction/introduction.md): Welcome to TimeGPT - The foundational model for time series forecasting and anomaly detection
- [TimeGPT Subscription Plans](https://nixtla-docs-broken-links-fix.mintlify.app/introduction/timegpt_subscription_plans.md): Overview of TimeGPT's Enterprise subscription plans with deployment options, support, and trial details.
- [Why TimeGPT?](https://nixtla-docs-broken-links-fix.mintlify.app/introduction/why_timegpt.md): Understand the benefits of using TimeGPT for time series analysis.
- [Date Features](https://nixtla-docs-broken-links-fix.mintlify.app/reference/date_features.md): Use holidays flags and special dates to improve your accuracy
- [SDK Reference](https://nixtla-docs-broken-links-fix.mintlify.app/reference/sdk_reference.md)
- [TimeGPT Excel Add-in (Beta)](https://nixtla-docs-broken-links-fix.mintlify.app/reference/timegpt_excel_add_in_beta_.md): Use TimeGPT from Microsoft Excel
- [TimeGPT in R](https://nixtla-docs-broken-links-fix.mintlify.app/reference/timegpt_in_r.md): Using TimeGPT for time series forecasting in the R programming language
- [TimeGEN-1 Quickstart (Azure)](https://nixtla-docs-broken-links-fix.mintlify.app/setup/azureai.md): Quickstart guide to deploy and use TimeGEN-1 on Azure with the Nixtla Python SDK for time series forecasting.
- [Docker Image for TimeGPT](https://nixtla-docs-broken-links-fix.mintlify.app/setup/docker.md): Learn how to access TimeGPT via a Docker image
- [Python Wheel for TimeGPT](https://nixtla-docs-broken-links-fix.mintlify.app/setup/python_wheel.md): Learn how to access TimeGPT via a Python wheel
- [Setting up your API key](https://nixtla-docs-broken-links-fix.mintlify.app/setup/setting_up_your_api_key.md): Learn how to securely configure your Nixtla SDK API key using direct code or environment variables.
- [Bitcoin Price Prediction](https://nixtla-docs-broken-links-fix.mintlify.app/use_cases/bitcoin_price_prediction.md): Learn how to use TimeGPT to predict Bitcoin prices and evaluate forecast accuracy.
- [Forecasting Energy Demand](https://nixtla-docs-broken-links-fix.mintlify.app/use_cases/forecasting_energy_demand.md): Learn how TimeGPT accurately predicts electricity consumption patterns
- [Forecasting Intermittent Demand](https://nixtla-docs-broken-links-fix.mintlify.app/use_cases/forecasting_intermittent_demand.md): Learn how to forecast sporadic demand patterns for inventory management.
- [Forecasting Web Traffic](https://nixtla-docs-broken-links-fix.mintlify.app/use_cases/forecasting_web_traffic.md): Learn how to predict website traffic patterns using TimeGPT.
- [What-If Forecasting: Price Effects in Retail](https://nixtla-docs-broken-links-fix.mintlify.app/use_cases/what_if_forecasting_price_effects_in_retail.md): Learn how to use TimeGPT to forecast sales scenarios with different pricing strategies.

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