release: v0.0.26 (#341)

Author: AzulGarza

docs/changelogs/index.md

@@ -2,7 +2,7 @@
 
 Welcome to the TimeCopilot Changelog. Here, you will find a comprehensive list of all the changes, updates, and improvements made to the TimeCopilot project. This section is designed to keep you informed about the latest features, bug fixes, and enhancements as we continue to develop and refine the TimeCopilot experience. Stay tuned for regular updates and feel free to explore the details of each release below.
 
-
+- [v0.0.26](v0.0.26.md)
 - [v0.0.25](v0.0.25.md)
 - [v0.0.24](v0.0.24.md)
 - [v0.0.23](v0.0.23.md)

docs/changelogs/v0.0.26.md

@@ -1,6 +1,6 @@
 ### Features
 
-* **New neural models**: Added 3 new auto neural models: `AutoNBEATS`, `AutoDeepAR`, and `AutoPatchTST`. All support `quantiles` for probabilistic forecasts trained with `MQLoss` and follow the same interface as the existing `AutoNHITS` and `AutoTFT`.
+* **New neural models**: Added 3 new auto neural models: `AutoNBEATS`, `AutoDeepAR`, and `AutoPatchTST`. All support `quantiles` for probabilistic forecasts trained with `MQLoss` and follow the same interface as the existing `AutoNHITS` and `AutoTFT`. See [#338](https://github.com/TimeCopilot/timecopilot/pull/338).
 
     ```python
     import pandas as pd
@@ -15,7 +15,7 @@
     fcst_df = model.forecast(df, h=12, quantiles=[0.1, 0.5, 0.9])
     ```
 
-* **New ML models**: Added 7 new auto ML models: `AutoLinearRegression`, `AutoXGBoost`, `AutoRidge`, `AutoLasso`, `AutoElasticNet`, `AutoRandomForest`, and `AutoCatboost`. All models support `quantiles` for probabilistic forecasts via conformal prediction and follow the same interface as the existing `AutoLGBM`.
+* **New ML models**: Added 7 new auto ML models: `AutoLinearRegression`, `AutoXGBoost`, `AutoRidge`, `AutoLasso`, `AutoElasticNet`, `AutoRandomForest`, and `AutoCatboost`. All models support `quantiles` for probabilistic forecasts via conformal prediction and follow the same interface as the existing `AutoLGBM`. See [#337](https://github.com/TimeCopilot/timecopilot/pull/337).
 
     ```python
     import pandas as pd
@@ -38,7 +38,7 @@
     fcst_df = model.forecast(df, h=12, quantiles=[0.1, 0.5, 0.9])
     ```
 
-* **Quantile forecasts for AutoLGBM, AutoNHITS, and AutoTFT**: These models now support quantile forecasts via the `quantiles` parameter. Pass a list of floats between 0 and 1 to receive additional output columns named `model-q-{percentile}`. Note that `level` is not supported for these models; use `quantiles` instead.
+* **Quantile forecasts for AutoLGBM, AutoNHITS, and AutoTFT**: These models now support quantile forecasts via the `quantiles` parameter. Pass a list of floats between 0 and 1 to receive additional output columns named `model-q-{percentile}`. Note that `level` is not supported for these models; use `quantiles` instead. See [#336](https://github.com/TimeCopilot/timecopilot/pull/336).
 
     - `AutoLGBM` computes prediction intervals via conformal prediction using cross-validation residuals.
     - `AutoNHITS` and `AutoTFT` are trained with [`MQLoss`](https://nixtla.github.io/neuralforecast/losses.pytorch.html) when quantiles are requested.
@@ -58,6 +58,12 @@
     # columns: unique_id, ds, AutoLGBM, AutoLGBM-q-10, AutoLGBM-q-50, AutoLGBM-q-90
     ```
 
+### Documentation
+
+* **Custom ensembles example**: Added the [Custom Ensembles](https://timecopilot.dev/examples/custom-ensembles/) notebook, showing how to combine multiple models into custom ensembles. See [#340](https://github.com/TimeCopilot/timecopilot/pull/340).
+
+* **Explaining foundation models and ensembles example**: Added the [Explaining Foundation Models and Ensembles](https://timecopilot.dev/examples/explaining-foundation-models-and-ensembles/) notebook. See [#340](https://github.com/TimeCopilot/timecopilot/pull/340).
+
 ---
 
 **Full Changelog**: https://github.com/TimeCopilot/timecopilot/compare/v0.0.25...v0.0.26

pyproject.toml

@@ -111,7 +111,7 @@ license = "MIT"
 name = "timecopilot"
 readme = "README.md"
 requires-python = ">=3.10"
-version = "0.0.25"
+version = "0.0.26"
 
 [project.optional-dependencies]
 distributed = [