Time Series Smoothing Improving Forecasting

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 amount fits best to make sufficiently accurate forecasts on it. This implies that the length or time series might be optimised. Hence, the objecti...

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Bibliographic Details
Main Author: Romanuke Vadim
Format: Article
Language:English
Published: Sciendo 2021-05-01
Series:Applied Computer Systems
Subjects:
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
Online Access:https://doi.org/10.2478/acss-2021-0008

Internet

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

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