Open AccessEMPIRICAL MODE DECOMPOSITION BASED ON THETA METHOD FOR FORECASTING DAILY STOCK PRICE
Author(s) -
Mohammad Raquibul Hossain,
Mohd Tahir Ismail
Publication year - 2020
Publication title -
journal of ict
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.217
H-Index - 10
eISSN - 2180-3862
pISSN - 1675-414X
DOI - 10.32890/jict2020.19.4.4
Subject(s) - hilbert–huang transform , autoregressive integrated moving average , stock exchange , computer science , econometrics , time series , stock market index , data mining , artificial intelligence , mathematics , machine learning , stock market , finance , economics , paleontology , filter (signal processing) , horse , biology , computer vision
Forecasting is a challenging task as time series data exhibit many features that cannot be captured by a single model. Therefore, many researchers have proposed various hybrid models in order to accommodate these features to improve forecasting results. This work proposed a hybrid method between Empirical Mode Decomposition (EMD) and Theta methods by considering better forecasting potentiality. Both EMD and Theta are efficient methods in their own ground of tasks for decomposition and forecasting, respectively. Combining them to obtain a better synergic outcome deserves consideration. EMD decomposed the training data from each of the five Financial Times Stock Exchange 100 Index (FTSE 100 Index) companies’ stock price time series data into Intrinsic Mode Functions (IMF) and residue. Then, the Theta method forecasted each decomposed subseries. Considering different forecast horizons, the effectiveness of this hybridisation was evaluated through values of conventional error measures found for test data and forecast data, which were obtained by adding forecast results for all component counterparts extracted from the EMD process. This study found that the proposed method produced better forecast accuracy than the other three classic methods and the hybrid EMD-ARIMA models.