CrostonTSB
- class hana_ml.algorithms.pal.tsa.exponential_smoothing.CrostonTSB(alpha=None, beta=None, forecast_num=None, method=None, accuracy_measure=None, ignore_zero=None, expost_flag=None, remove_leading_zeros=None, massive=False, group_params=None)
Croston TSB method (for Teunter, Syntetos & Babai) is a forecast strategy for products with intermittent demand. It is a modification of Croston's method.
It replaces the demand interval in Croston's method by demand probability which is updated every period. Compared to Croston's method, the forecast of TSB method is not biased and its probability forecast can be used to estimate the risk of obsolescence.
- Parameters:
- alphafloat, optional
Smoothing parameter for demand.
Defaults to 0.1.
- betafloat, optional
Smoothing parameter for probability.
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 a 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_.
- 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.
- 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.
- remove_leading_zerosbool, optional
False: Uses leading zero values in the input dataset when smoothing the probability;
True: Ignores leading zero values in the input dataset when smoothing the probability.
When it is set to 1, the leading zeros are ignored for calculating measure.
Defaults to False.
- massivebool, optional
Specifies whether or not to use massive mode of croston TSB.
True : massive mode.
False : single mode.
For parameter setting in massive mode, you could use both group_params (please see the example below) or the original parameters. Using original parameters will apply for all groups. However, if you define some parameters of a group, the value of all original parameter setting will be not applicable to such group.
An example is as follows:
In this example, as 'accuracy_measure' is set in group_params for Group_1, parameter setting of 'expost_flag' is not applicable to Group_1.
Defaults to False.
- group_paramsdict, optional
If massive mode is activated (
massive
is True), input data for croston TSB shall be divided into different groups with different parameters applied.An example is as follows:
Valid only when
massive
is True and defaults to None.
Examples
>>> cr = CrostonTSB(alpha=0.3, beta=0.1, forecast_num=10, method='constant', accuracy_measure=['mape'], expost_flag=True, ignore_zero=False, remove_leading_zeros=False)
Perform fit_predict():
>>> forecast = cr.fit_predict(data=df)
Output:
>>> forecast.collect() >>> cr.stats_.collect() >>> cr.metrics_.collect()
- Attributes:
- forecast_DateFrame
Forecast values.
- stats_DataFrame
Statistics analysis.
- metrics_DataFrame
Metrics Value.
- error_msg_DataFrame
Error message. Only valid if
massive
is True when initializing a 'CrostonTSB' instance.
Methods
fit_predict
(data[, key, endog, group_key])Fit and predict based on the given time series.
Get the model metrics.
Get the score metrics.
- fit_predict(data, key=None, endog=None, group_key=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.
In single mode, defaults to the first column of data if the index column of data is not provided. Otherwise, defaults to the index column of data.
In massive mode, defaults to the first-non group key column of data if the index columns of data is not provided. Otherwise, defaults to the second of index columns of data and the first column of index columns is group_key.
- endogstr, optional
The column of series to be fitted and predicted.
In single mode, defaults to the first non-ID column. In massive mode, defaults to the first non group_key, non key column.
- group_keystr, optional
The column of group_key. The data type can be INT or NVARCHAR/VARCHAR. If data type is INT, only parameters set in the group_params are valid.
This parameter is only valid when
massive
is True.Defaults to the first column of data if the index columns of data is not provided. Otherwise, defaults to the first column of index columns.
- Returns:
- DataFrame
Forecast values.
- get_model_metrics()
Get the model metrics.
- Returns:
- DataFrame
The model metrics.
- get_score_metrics()
Get the score metrics.
- Returns:
- DataFrame
The score metrics.
Inherited Methods from PALBase
Besides those methods mentioned above, the CrostonTSB class also inherits methods from PALBase class, please refer to PAL Base for more details.