Escalation of Forecasting Accuracy through Linear Combiners of Predictive Models

Chandra Nayak, Sarat (2019) Escalation of Forecasting Accuracy through Linear Combiners of Predictive Models. EAI Endorsed Transactions on Scalable Information Systems, 6 (23): e2. ISSN 2032-9407

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Precise and proficient modelling and forecasting financial time series has been paying attention of researchers, which leads to the development of various statistical and machine learning based models. Accuracy of a particular method is problem and domain specific, hence identifying best method is controversial. To boost up overall accuracies and minimizing risk of model selection, combination of outputs of different models has been recommended in the literature. This work presents a linear combiner of five predictive models i.e. ARIMA, RBFNN, MLP, SVM, and FLANN for improving prediction accuracy. Four statistical methods i.e. trimmed mean, simple average, median, and an error based method are used for suitable choice of combining weights. The individual forecasts and the linear combiner are used separately to predict closing price of five stock markets and exchange rate of five global markets. Extensive simulation work demonstrates the feasibility and supremacy of the linear combiner.

Item Type: Article
Uncontrolled Keywords: combining forecasts; ensemble method; artificial neural network; stock market prediction; financial time series forecasting; exchange rate forecasting; multilayer perceptron
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
QA75 Electronic computers. Computer science
Depositing User: EAI Editor II.
Date Deposited: 08 Oct 2020 13:54
Last Modified: 08 Oct 2020 13:54

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