DoubleExponentialSmoothing
- class hana_ml.algorithms.pal.tsa.exponential_smoothing.DoubleExponentialSmoothing(alpha=None, beta=None, forecast_num=None, phi=None, damped=None, accuracy_measure=None, ignore_zero=None, expost_flag=None, prediction_confidence_1=None, prediction_confidence_2=None)
Double exponential smoothing is suitable to model the time series with trend but without seasonality. In the model there are two kinds of smoothed quantities: smoothed signal and smoothed trend.
- Parameters
- alphafloat, optional
Weight for smoothing. Value range: 0 < alpha < 1.
Defaults to 0.1.
- betafloat, optional
Weight for the trend component. Value range: 0 < beta < 1.
Defaults to 0.1.
- forecast_numint, optional
Number of values to be forecast.
Defaults to 0.
- phifloat, optional
Value of the damped smoothing constant phi (0 < phi < 1).
Defaults to 0.1.
- dampedbool, optional
Specifies whether or not to use damped trend method.
False: No, uses the Holt's linear trend method.
True: Yes, use damped trend method.
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_measureis "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 DoubleExponentialSmoothing:
>>> df.collect() ID RAW_DATA 1 143.0 2 152.0 3 161.0 4 139.0
21 223.0 22 242.0 23 239.0 24 266.0
Create a DoubleExponentialSmoothing instance:
>>> desm = DoubleExponentialSmoothing(alpha=0.501, beta=0.072, forecast_num=6, phi=None, damped=None, accuracy_measure='mse', ignore_zero=None, expost_flag=None, prediction_confidence_1=0.8, prediction_confidence_2=0.95)
Perform fit_predict on the given data:
>>> desm.fit_predict(data=df)
Output:
>>> desm.forecast_.collect().set_index('TIMESTAMP').head(3) TIMESTAMP VALUE PI1_LOWER PI1_UPPER PI2_LOWER PI2_UPPER 2 152 NaN NaN NaN NaN 3 161 NaN NaN NaN NaN 4 170 NaN NaN NaN NaN
>>> desm.stats_.collect() STAT_NAME STAT_VALUE 0 MSE 274.8960228
- Attributes
- forecast_DataFrame
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.