<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>The presence of an outlier at the starting point of a univariate time series negatively influences the forecasting accuracy. The starting outlier is effectively removed only by making it equal to the second time point value. The forecasting accuracy is significantly improved after the removal. The favorable impact of the starting outlier removal on the time series forecasting accuracy is strong. It is the least favorable for time series with exponential rising. In the worst case of a time series, on average only 7 % to 11 % forecasts after the starting outlier removal are worse than they would be without the removal.</p>
</abstract>

The presence of an outlier at the starting point of a univariate time series negatively influences the forecasting accuracy. The starting outlier is effectively removed only by making it equal to the second time point value. The forecasting accuracy is significantly improved after the removal. The favorable impact of the starting outlier removal on the time series forecasting accuracy is strong. It is the least favorable for time series with exponential rising. In the worst case of a time series, on average only 7 % to 11 % forecasts after the starting outlier removal are worse than they would be without the removal.

Impact of Starting Outlier Removal on Accuracy of Time Series Forecasting

Maritime Technical Journal

This work is licensed under the Creative Commons Attribution 4.0 International License.

{"article-title":"Impact of Starting Outlier Removal on Accuracy of Time Series Forecasting"}

The presence of an outlier at the starting point of a univariate time series negatively influences the forecasting accuracy. The starting outlier is...

Impact of Starting Outlier Removal on Accuracy of Time Series Forecasting

Online veröffentlicht: 08. März 2022

Seitenbereich: 1 - 15

DOI: https://doi.org/10.2478/sjpna-2022-0001

Schlüsselwörter
time series forecasting, outlier, ARIMA, forecasting accuracy, RMSE, MaxAE

© 2020 Vadim Romanuke, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Impact of Starting Outlier Removal on Accuracy of Time Series Forecasting

Online veröffentlicht: 08. März 2022

Seitenbereich: 1 - 15

DOI: https://doi.org/10.2478/sjpna-2022-0001

Schlüsselwörtertime series forecasting, outlier, ARIMA, forecasting accuracy, RMSE, MaxAE

© 2020 Vadim Romanuke, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Schlüsselwörter
time series forecasting, outlier, ARIMA, forecasting accuracy, RMSE, MaxAE