Three stage fusion for effective time series forecasting using Bi-LSTM-ARIMA and improved DE-ABC algorithm.

Fusion is a state-of-the-art technique to observe the behavioral pattern from time series data. Fusion models efficiently and effectively interpret both linear and nonlinear patterns that are the constraints of an individual model due to feature limitations. In this paper, a three-stage fusion model is proposed to handle time series data and improve stock market forecasting accuracy. In the first phase of fusion, stock market inputs that are constituted with historical data and market sentiments of the targeted stock are pooled along with established technical indicators of the stock market. Market sentiments are examined through sentiment polarity index using big data platform Hadoop. In the second phase, Auto Regressive Integrated Moving Average (ARIMA) and Long Short-Term Memory (LSTM) are combined to observe linear and nonlinear features of the final stock dataset. In the third phase, an improved Artificial Bee Colony (ABC) algorithm using differential evolution (DE) is examined for the hyperparameter selection of the proposed DE-ABC-Bi-LSTM-ARIMA model for the stock market prediction. In this paper, experiments are performed on established and diversified reported historical datasets Dow Jones Industrial Average index, Nikkei 225 (N225) index, S&P 500 index and NASDAQ GS index. The proposed fusion model DE-ABC-Bi-LSTM-ARIMA outperformed the benchmark models used in this paper. [ABSTRACT FROM AUTHOR]