A Hybrid Model for Financial Time Series Forecasting—Integration of EWT, ARIMA With The Improved ABC Optimized ELM

He Yu,Ruan Sumei,Zhao Shuping,Li Jing Ming

doi:10.1109/access.2020.2987547

Abstract

The practical significances and complexities of financial time series analysis induce highly demand more reliable hybrid model that denoised the data efficiently, handled with both linear and nonlinear patterns in the data, to achieve more accurate results. This paper suggests a new forecasting hybrid model for financial time series data combined Empirical Wavelet Transform (EWT) technique with improved Artificial Bee Colony (ABC) algorithm, Extreme Learning Machine (ELM) neural network, and Auto-Regressive Integrated Moving Average (ARIMA) linear analysis algorithm. The EWT is used to decompose and denoise the data to reconstruct the data more suitable for forecast. The improvement of the ABC algorithm is according to the Good Point Sets (GPS) theory and adaptive Elite-based Opposition (EO) strategy (GPS-EO-ABC) to overcome the drawbacks of the original algorithm and enhance the optimization performance. The optimized ELM with GPS-EO-ABC, as well as the ARIMA, are utilized independently to generated different forecasting results and combined by the weight-based procession. We testify the performance of the proposed improved ABC algorithm by ten benchmark functions, simulating the proposed forecasting models by three financial time-series datasets. The results indicate that: (1) The proposed algorithm shows outstanding capacities in parameter optimization. The optimized ELM generated more stable and precise results compared with original ELM, ABC-ELM, single LSTM, and ANN; (2) The proposed hybrid model has not only effectiveness but also efficiencies in denoising data, correcting outliers, coordinating both linear and nonlinear patterns, its performance in financial time series forecasting is more excellent than existing hybrid models.

Highlights

The statistical tools or techniques that reveal the rules and forecast the futures of phenomena through financial time-series data have a guiding signature for both governments and enterprises to predict revenues and costs, to evade financial risks available
The reconstructed data yielded to individual forecasting models, including Auto-Regressive Integrated Moving Average (ARIMA), and the Extreme Learning Machine (ELM) improved by Good Point Sets (GPS)-Elite-based Opposition (EO)-Artificial Bee Colony (ABC) to obtain individual outcomes
These results indicated the optimized ELM model based on GPS and EO have a significant improvement in the accuracy in most cases

Summary

Introduction

The statistical tools or techniques that reveal the rules and forecast the futures of phenomena through financial time-series data have a guiding signature for both governments and enterprises to predict revenues and costs, to evade financial risks available. Financial time-series analysis has always been the frontier field of financial engineering and enterprise risk management [1]. As a peculiar time-series data, financial time series data which derived from stock. (2) Most data collected with noise; (3) Inherent non-linear relationships among data. Data decomposing, data denoising, and dealing with nonlinear data patterns properly are core points of putting financial time-series models into the application. Traditional time series analysis model such as Auto-Regressive Integrated Moving Average (ARIMA) [4], dynamics models such.

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Conclusion