BrownExponentialSmoothing
- class hana_ml.algorithms.pal.tsa.exponential_smoothing.BrownExponentialSmoothing(alpha=None, delta=None, forecast_num=None, adaptive_method=None, accuracy_measure=None, ignore_zero=None, expost_flag=None, prediction_confidence_1=None, prediction_confidence_2=None)
Brown exponential smoothing is suitable to model the time series with trend but without seasonality. Both non-adaptive and adaptive brown linear exponential smoothing are provided in PAL.
- Parameters:
- alphafloat, optional
The smoothing constant alpha for brown exponential smoothing or the initialization value for adaptive brown exponential smoothing (0 < alpha < 1).
Defaults to 0.1 when Brown exponential smoothing
Defaults to 0.2 when Adaptive brown exponential smoothing
- deltafloat, optional
Value of weighted for At and Mt.
Only valid when
adaptive_method
is True.Defaults to 0.2
- forecast_numint, optional
Number of values to be forecast.
Defaults to 0.
- adaptive_methodbool, optional
False: Brown exponential smoothing.
True: Adaptive brown exponential smoothing.
Defaults to False.
- 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.
- prediction_confidence_1float, optional
Prediction confidence for interval 1.
Only valid when the upper and lower columns are provided in the result table.
Defaults to 0.8.
- prediction_confidence_2float, optional
Prediction confidence for interval 2.
Only valid when the upper and lower columns are provided in the result table.
Defaults to 0.95.
Examples
Input dataframe df for BrownExponentialSmoothing:
>>> df.collect() ID RAWDATA 1 143.0 2 152.0 3 161.0 4 139.0 5 137.0
21 223.0 22 242.0 23 239.0 24 266.0
Create BrownExponentialSmoothing instance:
>>> brown_exp_smooth = BrownExponentialSmoothing(alpha=0.1, delta=0.2, forecast_num=6, adaptive_method=False, accuracy_measure='mse', ignore_zero=0, expost_flag=1)
Perform fit on the given data:
>>> brown_exp_smooth.fit_predict(data=df)
Output:
>>> brown_exp_smooth.forecast_.collect().set_index('TIMESTAMP').head(6) TIMESTAMP VALUE 2 143.00000 3 144.80000 4 148.13000 5 146.55600 6 144.80550 7 150.70954
>>> brown_exp_smooth.stats_.collect() STAT_NAME STAT_VALUE MSE 474.142004
- 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 BrownExponentialSmoothing class also inherits methods from PALBase class, please refer to PAL Base for more details.