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

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

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Stock Market Forecasting Methods Time Series Analysis and Forecasting Machine Learning and ELM

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.

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References

2003Time series forecasting using a hybrid ARIMA and neural network model 1969The Combination of Forecasts 2005Analysis of Financial Time Series 2014Stock Price Prediction Using the ARIMA Model 2017A deep learning framework for financial time series using stacked autoencoders and long-short term memory 2016Computational Intelligence and Financial Markets: A Survey and Future Directions 2012Support vector regression with chaos-based firefly algorithm for stock market price forecasting 2011A hybrid stock selection model using genetic algorithms and support vector regression

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