Version 1
: Received: 27 February 2024 / Approved: 27 February 2024 / Online: 28 February 2024 (11:27:50 CET)
How to cite:
Li, X. Time Series Forecasting with Missing Values Using Multiple Models: An Application to Wind Energy Systems. Preprints2024, 2024021547. https://doi.org/10.20944/preprints202402.1547.v1
Li, X. Time Series Forecasting with Missing Values Using Multiple Models: An Application to Wind Energy Systems. Preprints 2024, 2024021547. https://doi.org/10.20944/preprints202402.1547.v1
Li, X. Time Series Forecasting with Missing Values Using Multiple Models: An Application to Wind Energy Systems. Preprints2024, 2024021547. https://doi.org/10.20944/preprints202402.1547.v1
APA Style
Li, X. (2024). Time Series Forecasting with Missing Values Using Multiple Models: An Application to Wind Energy Systems. Preprints. https://doi.org/10.20944/preprints202402.1547.v1
Chicago/Turabian Style
Li, X. 2024 "Time Series Forecasting with Missing Values Using Multiple Models: An Application to Wind Energy Systems" Preprints. https://doi.org/10.20944/preprints202402.1547.v1
Abstract
When dealing with limited sample sizes or missing values in time series data,
achieving high accuracy in forecasting becomes challenging. This challenge is
particularly common in wind energy forecasting, where many wind farms lack
complete data. In this paper, we employ the widely-used ARIMA (Autoregressive
Integrated Moving Average) model for time series forecasting. To address the
difficulties posed by limited sample sizes and missing values, we adopt a
unique approach by utilizing multiple ARIMA models for different datasets. A
key distinction from other multiple models methods for time series forecasting
lies in each model within our approach using its own dataset, making the
fusion process more intricate. To overcome this, we incorporate neural
networks and two techniques for model fusion: direct fusion with a learning
mechanism and compensation for missing data through training. For each ARIMA
model, we introduce a noise estimation method to circumvent the white noise
assumptions inherent in ARIMA models. Finally, we apply these methods to wind
energy prediction, and our experimental results showcase a significant
enhancement in forecasting accuracy.
Keywords
time series forecasting; ARIMA; neural networks; transfer learning; wind energy systems
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
