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

<p>Both statistical and neural network methods may fail in forecasting time series even operating on a great amount of data. It is an open question of which amount fits best to make sufficiently accurate forecasts on it. This implies that the length or time series might be optimised. Hence, the objective is to improve the quality of forecasting by an assumption that parameters are set nearly at their optimal values. To achieve objective, the two types of the benchmark time series are considered: sine-shaped series and random-like series with repeatability. Trend, seasonality, and decay properties embedded into each type. Based on the benchmark of 24 time series models, it is ascertained that, for improving the forecasting, the time series should be smoothed and then downsampled. These operations can be fulfilled successively until the improvement fails. If preliminary smoothing worsens forecasts, the raw time series is straightforwardly downsampled until the forecasting accuracy starts dropping. However, if time series has a visible property of being noised, the preliminary smoothing is strongly recommended.</p>
</abstract>

Both statistical and neural network methods may fail in forecasting time series even operating on a great amount of data. It is an open question of which amount fits best to make sufficiently accurate forecasts on it. This implies that the length or time series might be optimised. Hence, the objective is to improve the quality of forecasting by an assumption that parameters are set nearly at their optimal values. To achieve objective, the two types of the benchmark time series are considered: sine-shaped series and random-like series with repeatability. Trend, seasonality, and decay properties embedded into each type. Based on the benchmark of 24 time series models, it is ascertained that, for improving the forecasting, the time series should be smoothed and then downsampled. These operations can be fulfilled successively until the improvement fails. If preliminary smoothing worsens forecasts, the raw time series is straightforwardly downsampled until the forecasting accuracy starts dropping. However, if time series has a visible property of being noised, the preliminary smoothing is strongly recommended.

Time Series Smoothing Improving Forecasting

Applied Computer Systems

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

{"article-title":"Time Series Smoothing Improving Forecasting"}

Both statistical and neural network methods may fail in forecasting time series even operating on a great amount of data. It is an open question of which...

Time Series Smoothing Improving Forecasting

Publié en ligne: 04 juin 2021

Pages: 60 - 70

DOI: https://doi.org/10.2478/acss-2021-0008

Mots clés
Autoregressive integrated moving average (ARIMA), downsampling, forecasting accuracy, long short-term memory (LSTM), maximum absolute error (MaxAE), root-mean-square error (RMSE), smoothing, time series forecasting

© 2021 Vadim Romanuke, published by Sciendo

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

Time Series Smoothing Improving Forecasting

Publié en ligne: 04 juin 2021

Pages: 60 - 70

DOI: https://doi.org/10.2478/acss-2021-0008

Mots clésAutoregressive integrated moving average (ARIMA), downsampling, forecasting accuracy, long short-term memory (LSTM), maximum absolute error (MaxAE), root-mean-square error (RMSE), smoothing, time series forecasting

© 2021 Vadim Romanuke, published by Sciendo

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

Mots clés
Autoregressive integrated moving average (ARIMA), downsampling, forecasting accuracy, long short-term memory (LSTM), maximum absolute error (MaxAE), root-mean-square error (RMSE), smoothing, time series forecasting