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Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

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Published on: July 3, 2020

A novel general-purpose hybrid model for time series forecasting.

Yun Yang¹, ChongJun Fan¹, HongLin Xiong¹

¹University of Shanghai for Science and Technology, Shanghai, China.

Applied Intelligence (Dordrecht, Netherlands)

|November 12, 2021

Summary

This study introduces a new hybrid model for accurate data flow prediction in industrial automation. The Recursive Empirical Mode Decomposition-Long Short-Term Memory (REMD-LSTM) model enhances prediction accuracy and versatility across diverse data types.

Keywords:

Data decomposition REMD-LSTM Time series prediction

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Area of Science:

Industrial Automation
Data Science
Machine Learning

Background:

Accurate data flow prediction is crucial for industrial automation but challenging due to diverse data types.
Traditional time series models struggle with versatility and accuracy across different data types.

Purpose of the Study:

To propose a novel hybrid time-series prediction model that improves both accuracy and versatility.
To address limitations of traditional decomposition methods in time series analysis.

Main Methods:

A new Recursive Empirical Mode Decomposition (REMD) method is proposed to overcome marginal effects and mode confusion.
Data streams are decomposed into intrinsic mode functions (IMFs) using REMD.
Long Short-Term Memory (LSTM) networks predict each IMF subsequence, with results aggregated for the final prediction.

Main Results:

The proposed REMD-LSTM model demonstrates over 20% improvement in prediction accuracy compared to standard LSTM.
The model achieves the highest prediction accuracy across all tested diverse data sets.
Experimental results confirm superior accuracy and versatility over state-of-the-art models.

Conclusions:

The REMD-LSTM model offers significant advantages in prediction accuracy and versatility for industrial automation.
This hybrid approach effectively handles diverse data types, outperforming existing methods.