Data Mining of Multiple Nonstationary Time Series
نویسندگان
چکیده
A data mining method for synthesizing multiple time series is presented. Based on a single time series algorithm, the method embeds multiple time series into a phase space. The reconstructed state space allows temporal pattern extraction and local model development. Using an a priori data mining objective, an optimal local model is chosen for short-term forecasting. For the same sampling period, multiple time series embedding produces better temporal patterns than single time series embedding. The method is applied to a financial time series.
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