--- title: "Changelog" description: "Complete list of changes for each version of the Nixtla client." icon: "clipboard" --- ## Changelog Overview Below you’ll find the complete list of changes for each version of the Nixtla client. Expand any version to see details about new features, improvements, changes, or deprecations, along with links to full release notes. ### Feature Enhancements **Online anomaly detection** We introduce the `online_anomaly_detection` method, which allows you to define a `detection_size` on which to look for anomalies. [See full changelog here](https://github.com/Nixtla/nixtla/releases/v0.6.6) ### Feature Enhancements You can now run an isolated fine-tuning process, save the model, and use it afterward in all of our methods:
All requests above 1MB are automatically compressed using [zstd](https://github.com/facebook/zstd), which helps when sending large data volumes or with slower connections. Set `refit=False` to fine-tune the model only on the first window in `cross_validation`. This significantly decreases computation time.
[See full changelog here](https://github.com/Nixtla/nixtla/releases/v0.6.5)
### Feature Enhancements The client now supports integer timestamps and frequencies, and custom pandas timestamps (including [CustomBusinessHour](https://pandas.pydata.org/docs/reference/api/pandas.tseries.offsets.CustomBusinessHour.html)). You can programmatically retrieve your current API call count and limits using the new `usage` method. The `cross_validation` method now accepts the `hist_exog_list` parameter, enabling definition of historical exogenous features. [See full changelog here](https://github.com/Nixtla/nixtla/releases/v0.6.4) ### Feature Enhancements **Fine-tune depth** Specify the fine-tuning depth through the `finetune_depth` parameter in `forecast` and `cross_validation`. [See full changelog here](https://github.com/Nixtla/nixtla/releases/v0.6.2) ### Feature Enhancements The client now uses V2 API endpoints, providing lower latency. Payload serialization now uses [orjson](https://github.com/ijl/orjson) for performance improvements, especially with exogenous features. Historical exogenous features (`hist_exog_list`) are supported in the `forecast` method. Activate feature contributions by setting: ```python Feature Contributions Example feature_contributions = True ``` in the `forecast` method. [See full changelog here](https://github.com/Nixtla/nixtla/releases/v0.6.0) ### Feature Enhancements **Cross validation endpoint** The `cross_validation` method now performs a single API call instead of individual calls per window. [See full changelog here](https://github.com/Nixtla/nixtla/releases/v0.5.0) ### Changes & Deprecations **Important:** The `nixtlats` package has been deprecated in favor of the `nixtla` package. [See full changelog here](https://github.com/Nixtla/nixtla/releases/v0.4.0) ### Changes & Deprecations **Deprecation of `TimeGPT` class** Replace `TimeGPT` with `NixtlaClient`. Also note: - Parameters renamed: `token` → `api_key`, `environment` → `base_url` - Method renamed: `validate_token` → `validate_api_key` - Update environment variables to match new parameter names. [See full changelog here](https://github.com/Nixtla/nixtla/releases/v0.3.0) ### Changes & Deprecations Renamed fine-tuning parameter: - From `finetune_steps` to `fewshot_steps` (This change was later reverted for compatibility reasons). [See full changelog here](https://github.com/Nixtla/nixtla/releases/v0.2.0) ### Feature Enhancements Quantile forecasts added to: - `forecast` - `cross_validation` [See full changelog here](https://github.com/Nixtla/nixtla/releases/v0.1.21) ### Feature Enhancements Enhanced fine-tuning capability with new parameters: - `finetune_loss` - `finetune_steps` [See full changelog here](https://github.com/Nixtla/nixtla/releases/v0.1.20) ### Feature Enhancements Implemented `num_partitions` parameter for improved resource optimization. [See full changelog here](https://github.com/Nixtla/nixtla/releases/v0.1.19) ### Feature Enhancements Support for longer forecast horizons with our specialized model. Evaluate model performance across different time periods. Using parameters: - `max_retries` - `retry_interval` - `max_wait_time` Environment tokens are now handled automatically. Common questions and answers to help you get started. [See full changelog here](https://github.com/Nixtla/nixtla/releases/v0.1.18)