Abstract
In this paper, a novel time series classification approach, which using auto regressive integrated moving average model (ARIMA) features and Adaptive Boosting (AdaBoost) classifications. ARIMA is particularly suitable for distinguishing time series signal and Adaboost is suitable for features classification. The simulation results have shown that the algorithm is feasible. And this method is more accurate than many existing method in multiple time series problems.
Highlights
Time-series data arise in many fields including finance, signal processing, speech recognition and medicine
Time series classification(TSC) is a difficult problem, and the time series featrues are more unstable than image feature, so it’s difficult to extract key features from them
We have presented time series classifier based on auto regressive integrated moving average model (ARIMA) and AdaBoost
Summary
Time-series data arise in many fields including finance, signal processing, speech recognition and medicine. A standard approach of time-series problems usually requires feed time series features into a machine learning algorithm and get goal result [1]. Engineering of features generally requires some domain knowledge of the discipline where the data has originated from. If one is dealing with signals (i.e. classification of EEG signals), possible features would involve power spectra at various frequency bands and several other specialized statistical properties. Time series classification(TSC) is a difficult problem, and the time series featrues are more unstable than image feature, so it’s difficult to extract key features from them. Feature extraction of time series affects the final classification effect, so the current accuracy of the results is still very low
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