In this version:
- added Riemann geometry feature extractor
- we migrated from PIP to Poetry dependancy management
- refactored and updated `examples` directory
- introduced ssa+ar and glm models for time-series forecasting
- implemented channel filtration method and n-components selection after RSVD
- minor fixes and improvements
industrial
**Refactoring feature extraction models. Quantile/topological/recurrence feature extraction models was refactored**
- [x] Models have been redesigned and brought to a "single" type of class, in which there are 2 key methods: **_transform()_** (for calling inside the Pipeline constructor Pipeline) and the **_generate_features_from_ts()_** method for calling these models from the outside. The **__transform()_** method inherits from the abstract **_BaseExtractor_** class.
- [x] For these models, 2 hyperparameters were derived and obtained: **_window width and stride_**. For the quantile model: window width is the width of the window in which the features are calculated, stride is the step size of the sliding window. For recurrent and topological models: window width and stride are the parameters used in the time series Gankelization (converting a series into a trajectory matrix).
- [x] Usage examples are in examples/pipeline_examples/time_series/ts_classification
**Refactoring basis models (fedot_ind/core/operation/transformation/basis). Eigen/fourier/wavelet basis models was refactored**
- [x] Models have been redesigned and brought to a "single" type of class, in which there are 2 key methods: **_transform()_** (for calling inside the Pipeline constructor Pipeline) and the **_get_1d_basis() and _get_multidim_basis()_** method for calling these models from the outside. The **__transform()_** method inherits from the abstract **_AbstractBasis_** class.
- [x] For EigenBasis a new algorithm for low-rank approximation based on Krylov subspaces was developed. This algorithm is located in _**fedot_ind/core/operation/decomposition/matrix_decomposition/power_iteration_decomposition.py**_ . Due to this algorithm, it was possible to significantly reduce the calculation time of the trajectory matrix eigenvectors basis of the time series, achieve better separability of time series components, and also it became possible to work with "long" time series (>100,000 elements)
- [x] Usage examples are in examples/pipeline_examples/time_series/ts_classification
**New type of time series machine learning task. Time Series Forecasting (TSF).**
- [x] New forecasting model: **_Singular Spectrum Analysis (SSA)_** based on EigenBasis model.
- [x] This model uses EigenBasis to decompose a time series, represented as a trajectory matrix, into components (trend, seasonality, noise). The resulting components are predicted using standard models (e.g. **_ARIMA_**).
- [x] Usage examples are in examples/pipeline_examples/time_series/ts_forecasting
**New type of time series machine learning task: **T**ime **S**eries **E**xtinct **R**egression (TSER).**
- [x] TSER involves a predictive model built on time series to predict a real-valued variable distinct from the training input series.
- [x] Usage examples are in examples/api_examples/time_series/ts_regression
**Unit and Integration tests was refactored.**