Metadata-Version: 2.1
Name: skits
Version: 0.1.2
Summary: scikit-learn-inspired time series
Home-page: https://github.com/EthanRosenthal/skits
Author: Ethan Rosenthal
Author-email: ethanrosenthal@gmail.com
License: MIT
Description: # skits
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        A library for
        **S**ci**K**it-learn-**I**nspired **T**ime **S**eries models.
        
        The primary goal of this library is to allow one to train time series prediction models using a similar API to `scikit-learn`. Consequently, similar to `scikit-learn`, this library consists of `preprocessors`, `feature_extractors`, and `pipelines`. 
        
        ## Installation
        
        Install with pip:
        
        ```commandline
        pip install skits
        ```
        
        ## Preprocessors
        
        The preprocessors expect to receive time series data, and then end up storing some data about the time series such that they can fully invert a transform. The following example shows how to create a `DifferenceTransformer` transform data, and then invert it back to its original form. The `DifferenceTransformer` subtracts the point shifted by `period` away from each point.
        
        ```python
        import numpy as np
        from skits.preprocessing import DifferenceTransformer
        
        y = np.random.random(10)
        # scikit-learn expects 2D design matrices,
        # so we duplicate the time series.
        X = y[:, np.newaxis] 
        
        dt = DifferenceTransformer(period=2)
        
        Xt = dt.fit_transform(X,y)
        X_inv = dt.inverse_transform(Xt)
        
        assert np.allclose(X, X_inv)
        ```
        
        ## Feature Extractors
        
        After all preprocessing transformations are completed, multiple features may be built out of the time series. These can be built via feature extractors, which one should combine together into a large `FeatureUnion`. Current features include autoregressive, seasonal, and integrated features (covering the AR and I of ARIMA models).
        
        
        ## Pipelines
        
        There are two types of pipelines. The `ForecasterPipeline` is for forecasting time series (duh). Specifically, one should build this pipeline with a regressor as the final step such that one can make appropriate predictions. The functionality is similar to a regular `scikit-learn` pipeline. Differences include the addition of a `forecast()` method along with a `to_scale` keyword argument to `predict()` such that one can make sure that their prediction is on the same scale as the original data.
        
        These classes are likely subject to change as they are fairly hacky right now. For example, one must transform both `X` and `y` for all transformations before the introduction of a `DifferenceTransformer`. While the pipeline handles this, one must prefix all of these transformations with `pre_` in the step names.
        
        Anywho, here's an example:
        
        ```python
        import numpy as np
        from sklearn.linear_model import LinearRegression
        from sklearn.preprocessing import StandardScaler
        from sklearn.pipeline import FeatureUnion
        
        from skits.pipeline import ForecasterPipeline
        from skits.preprocessing import ReversibleImputer
        from skits.feature_extraction import (AutoregressiveTransformer, 
                                              SeasonalTransformer)
                                       
        steps = [
            ('pre_scaling', StandardScaler()),
            ('features', FeatureUnion([
                ('ar_transformer', AutoregressiveTransformer(num_lags=3)),
                ('seasonal_transformer', SeasonalTransformer(seasonal_period=20)
            )])),
            ('post_features_imputer', ReversibleImputer()),
            ('regressor', LinearRegression(fit_intercept=False))
        ]
                                       
        l = np.linspace(0, 1, 101)
        y = 5*np.sin(2 * np.pi * 5 * l) + np.random.normal(0, 1, size=101)
        X = y[:, np.newaxis]
        
        pipeline = ForecasterPipeline(steps)
        
        pipeline.fit(X, y)
        y_pred = pipeline.predict(X, to_scale=True, refit=True)
        ```
        
        And this ends up looking like:
        
        ```python
        import matplotlib.pyplot as plt
        
        plt.plot(y, lw=2)
        plt.plot(y_pred, lw=2)
        plt.legend(['y_true', 'y_pred'], bbox_to_anchor=(1, 1));
        ```
        ![pred](pred.png)
        
        And forecasting looks like
        
        ```python
        start_idx = 70
        plt.plot(y, lw=2);
        plt.plot(pipeline.forecast(y[:, np.newaxis], start_idx=start_idx), lw=2);
        ax = plt.gca();
        ylim = ax.get_ylim();
        plt.plot((start_idx, start_idx), ylim, lw=4);
        plt.ylim(ylim);
        plt.legend(['y_true', 'y_pred', 'forecast start'], bbox_to_anchor=(1, 1));
        ```
        ![forecast](forecast.png)
Keywords: machine learning time series forecasting
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: Topic :: Scientific/Engineering :: Information Analysis
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Description-Content-Type: text/markdown
