ARIMA and ANN Approach for forecasting daily stock price fluctuations of industries in Colombo Stock Exchange, Sri Lanka

Abstract

Time series forecasting is regarded as the most successful criterion among several factors involved in the decision-making process to pick a correct prediction model. Improving predictability has become crucial for decision-makers and managers, especially time series forecasts, in various fields of science. Using K-mean clustering and Principle Component Analysis, the dataset is clustered based upon a central point selection and the Euclidian distance measurement. The results define the main contribution sector for CSE, and the business in the selected sector in the 2008-2017 period in accordance with the clustering results. In particular, ARIMA has demonstrated its performance in predicting the next lags in precision and accuracy. With regard to Colombo Stock Exchange (CSE), there are very few studies in the literature that have focused on new approaches to forecasts of high volatility stock price indexes. Different statistical methods and economic data techniques have been widely applied in the last decade in order to classify CSE's stock price, patterns and trade volumes. This article looks at the best sector and organization to invest in and discusses whether and how the deep-learning algorithms for time series data projection, such as the Back Propagation Neural Network, are better than traditional algorithms. The results show that Deep learning algorithms like BPNN outperform traditionally based algorithms like the model ARIMA. For ARIMA and ANN, MAPE values are 0.472206 and 0.1783333 respectively. MAE values are 29.6975 and 4.708423 respectively results for ARIMA and ANN. The MAE and MAPE values relative to ARIMA and BPNN, which suggests BPNN `s superiority to ARIMA.