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Short-term trend prediction in financial time series data

This paper presents a method to predict short-term trends in financial time series data found in the foreign exchange market. Trends in the Forex market appear with similar chart patterns. We approach the chart patterns in the financial markets from a discovery of motifs in a time series perspective. Our method uses a modified Zigzag technical indicator to segment the data and discover motifs, expectation maximization to cluster the motifs and support vector machines to classify the motifs and predict accurate trading parameters for the identified motifs. The available input data are adapted to each trading time frame with a sliding window. The accuracy of the prediction models is tested across several different currency pairs, spanning 5 years of historical data from 2010 to 2015. The experimental results suggest that using the Zigzag technical indicator to discover motifs that identify short-term trends in financial data results in a high prediction accuracy and trade profits.