Comparative Study of Artificial Neural Networks and Hidden Markov Model for Financial Time Series Prediction (The International Journal of Engineering & Information Technology (IJEIT), 2024)
Financial Time Series analysis and prediction isone of the interesting areas in which past data could be usedto anticipate and predict data an d information about future.There are many artificial intelligence approaches used in theprediction of time series, such as Artificial Neural Networks (ANN) and Hidden Markov Models (HMM). In this paperHMM and HMM approaches for predicting financial timeseries are presented. ANN and HMM are used to predicttime series that consists of highest and lowest Forex indexseries as input variable. Both of ANN and HMM are trained on the past dataset of the chosen currencies (such as EURO/USD which is used in this paper). The trained ANN andHMM are used to search for the variable of interestbehavioral data pattern from the past dataset. The obtained results was compared with real values from Forex (ForeignExchange) market database [1]. The power and predictiveability of the two models are evaluated on the basis of MeanSquare Error (MSE). The Experimental results obtained areencouraging, and it demonstrate that ANN and HMM can closely predict the currency market, with a small differentin predicting performance