# Timeseries#

## Datasets#

`mlinsights.timeseries.datasets.artificial_data`

(*dt1*, *dt2*, *minutes* = 1)

Generates articial data every minutes.

## Experimentation#

`mlinsights.timeseries.patterns.find_ts_group_pattern`

(*ttime*, *values*, *names*, *name_subset* = None, *per* = ‘week’, *unit* = ‘half-hour’, *agg* = ‘sum’, *estimator* = None, *fLOG* = None)

Clusters times series to find similar patterns.

## Manipulation#

`mlinsights.timeseries.agg.aggregate_timeseries`

(*df*, *index* = ‘time’, *values* = ‘y’, *unit* = ‘half-hour’, *agg* = ‘sum’, *per* = None)

Aggregates timeseries assuming the data is in a dataframe.

## Plotting#

`mlinsights.timeseries.plotting.plot_week_timeseries`

(*time*, *value*, *normalise* = True, *label* = None, *h* = 0.85, *value2* = None, *label2* = None, *daynames* = None, *xfmt* = ‘%1.0f’, *ax* = None)

Shows a timeseries dispatched by days as bars.

## Prediction#

The following function builds a regular dataset from a timeseries so that it can be used by machine learning models.

`mlinsights.timeseries.selection.build_ts_X_y`

The first class defined the template for all timeseries estimators. It deals with a timeseries ine one dimension and additional features.

`mlinsights.timeseries.base.BaseTimeSeries`

(*self*, *past* = 1, *delay1* = 1, *delay2* = 2, *use_all_past* = False, *preprocessing* = None)

Base class to build a predictor on timeseries. The class computes one or several predictions at each time, between

delay1anddelay2. It computes: withdin[delay1, delay2[and .

the first predictor is a dummy one: it uses the current value to predict the future.

`mlinsights.timeseries.dummies.DummyTimeSeriesRegressor`

(*self*, *estimator* = ‘dummy’, *past* = 1, *delay1* = 1, *delay2* = 2, *use_all_past* = False, *preprocessing* = None)

Dummy regressor for time series. Use past values as prediction.

The first regressor is an auto-regressor. It can be estimated with any regressor implemented in scikit-learn.

`mlinsights.timeseries.ARTimeSeriesRegressor`

(*self*, *estimator* = ‘dummy’, *past* = 1, *delay1* = 1, *delay2* = 2, *use_all_past* = False, *preprocessing* = None)

Base class to build a regressor on timeseries. The class computes one or several predictions at each time, between

delay1anddelay2. It computes: withdin[delay1, delay2[and .

The library implements one scoring function which compares the prediction to what a dummy predictor would do by using the previous day as a prediction.

`mlinsights.timeseries.metrics.ts_mape`

(*expected_y*, *predicted_y*, *sample_weight* = None)

Computes . It compares the prediction to what a dummy predictor would do by using the previous day as a prediction.