Financial time series predicting using machine learning algorithms - Tiong, Leslie Ching Ow * (2013)
Financial time series prediction is a challenging task due to the fluctuation of trading or economic exchange that is difficult to predict. Researchers from different fields have been attracted to perform several techniques for identifying reliability of the financial time series prediction. Finding of research papers, the financial trend patterns repeat itself in the history. Thus, this research motivates and aims to investigate the repeat behaviour and pattern of trends from the historical financial time series data, and utilise the strength of machine learning techniques to develop a promising financial time series predictor engine. In this research, two frameworks are proposed for financial time series prediction. In the first proposed framework, candlestick pattern is utilised as technical analysis method to identify the financial trends. Thereafter, Artificial Neural Network (ANN) and Support Vector Machine (SVM) algorithms are implemented separately to train with the trend patterns for predicting the movement direction of financial trends. In the second proposed framework, Linear Regression Line (LRL) is utilised to identify the trend patterns from historical financial time series, which is supported by ANN and SVM for classification process separately. Subsequently, Dynamic Time Warping (DTW) algorithm is utilised through brute force to predict the trend movement. The experimental results showed that the second proposed model is consistent with the hypothesis, which provides better accuracy of prediction. Therefore, the findings of this research help in improving the accuracy of prediction model.