Computational intelligence methods for data mining of causality extent in the time series Online publication date: Mon, 06-Aug-2018
by Lukáš Pichl; Taisei Kaizoji
International Journal of Computational Science and Engineering (IJCSE), Vol. 16, No. 4, 2018
Abstract: We adopt the support vector machine (SVM) and artificial neural network (ANN) for causality rate extraction. The dataset records all details of the futures contracts on the commodity of gasoline traded in Japan. By sampling the tick data at 1 min, 5 min, 10 min, 30 min, 1 hour and 1 day scales, we derive time series of varying causal degree. Trend predictions are computed by using the SVM binary classifier trained on 66.6% of the data using a five-step-back moving window which samples the log returns as the predictor data. From the testing data, we extract varying rates of causality degree, starting from the borderline of 50% up to the order of 60% in rare cases. The trend prediction analysis is complemented by the ANN method with four hidden layers. Overall, the market of the gasoline futures in Japan is found to be rather close to the efficient market hypothesis in comparison with other commodities markets.
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