Croston
- class hana_ml.algorithms.pal.tsa.exponential_smoothing.Croston(alpha=None, forecast_num=None, method=None, accuracy_measure=None, ignore_zero=None, expost_flag=None)
Croston method is a forecast strategy for products with intermittent demand. Croston method consists of two steps. First, separate exponential smoothing estimates are made of the average size of a demand. Second, the average interval between demands is calculated. This is then used in a form of the constant model to predict the future demand.
- Parameters
- alphafloat, optional
Value of the smoothing constant alpha (0 < alpha < 1).
Defaults to 0.1.
- forecast_numint, optional
Number of values to be forecast.
When it is set to 1, the algorithm only forecasts one value.
Defaults to 0.
- methodstr, optional
'sporadic': Use the sporadic method.
'constant': Use the constant method.
Defaults to 'sporadic'.
- accuracy_measurestr or list of str, optional
The metric to quantify how well a model fits input data. Options: "mpe", "mse", "rmse", "et", "mad", "mase", "wmape", "smape", "mape".
No default value.
Note
Specify a measure name if you want the corresponding measure value to be reflected in the output statistics self.stats_.
- ignore_zerobool, optional
False: Uses zero values in the input dataset when calculating "mpe" or "mape".
True: Ignores zero values in the input dataset when calculating "mpe" or "mape".
Only valid when
accuracy_measure
is "mpe" or "mape".Defaults to False.
- expost_flagbool, optional
False: Does not output the expost forecast, and just outputs the forecast values.
True: Outputs the expost forecast and the forecast values.
Defaults to True.
Examples
Input dataframe df for Croston:
>>> df.collect() ID RAWDATA 0 0.0 1 1.0 2 4.0 3 0.0 4 0.0 5 0.0 6 5.0 7 3.0 8 0.0 9 0.0 10 0.0
Create a Croston instance:
>>> croston = Croston(alpha=0.1, forecast_num=1, method='sporadic', accuracy_measure='mape')
Perform fit on the given data:
>>> croston.fit_predict(data=df)
Output:
>>> croston.forecast_.collect().set_index('ID').head(6) ID RAWDATA 0 0.000000 1 3.025000 2 3.122500 3 0.000000 4 0.000000 5 0.000000
>>> croston.stats_.collect() STAT_NAME STAT_VALUE MAPE 0.2432181818181818
- Attributes
- forecast_DateFrame
Forecast values.
- stats_DataFrame
Statistics analysis content.
Methods
Generate time series report.
fit_predict
(data[, key, endog])Fit and predict based on the given time series.
generate_html_report
([filename])Display function.
Display function.
- fit_predict(data, key=None, endog=None)
Fit and predict based on the given time series.
- Parameters
- dataDataFrame
Input data. At least two columns, one is ID column, the other is raw data.
- keystr, optional
The ID column.
Defaults to the first column of data if the index column of data is not provided. Otherwise, defaults to the index column of data.
- endogstr, optional
The column of series to be fitted and predicted.
Defaults to the first non-ID column.
- Returns
- DataFrame
Forecast values.
- build_report()
Generate time series report.
- property fit_hdbprocedure
Returns the generated hdbprocedure for fit.
- generate_html_report(filename=None)
Display function.
- generate_notebook_iframe_report()
Display function.
- property predict_hdbprocedure
Returns the generated hdbprocedure for predict.
Inherited Methods from PALBase
Besides those methods mentioned above, the Croston class also inherits methods from PALBase class, please refer to PAL Base for more details.