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Time Series Analysis Using Geometric Template Matching
March 2013 (vol. 35 no. 3)
pp. 740-754
J. Frank, Sch. of Comput. Sci., McGill Univ., Montreal, QC, Canada
S. Mannor, Dept. of Electr. Eng., Technion - Israel Inst. of Technol., Haifa, Israel
J. Pineau, Sch. of Comput. Sci., McGill Univ., Montreal, QC, Canada
D. Precup, Sch. of Comput. Sci., McGill Univ., Montreal, QC, Canada
We present a novel framework for analyzing univariate time series data. At the heart of the approach is a versatile algorithm for measuring the similarity of two segments of time series called geometric template matching (GeTeM). First, we use GeTeM to compute a similarity measure for clustering and nearest-neighbor classification. Next, we present a semi-supervised learning algorithm that uses the similarity measure with hierarchical clustering in order to improve classification performance when unlabeled training data are available. Finally, we present a boosting framework called TDEBOOST, which uses an ensemble of GeTeM classifiers. TDEBOOST augments the traditional boosting approach with an additional step in which the features used as inputs to the classifier are adapted at each step to improve the training error. We empirically evaluate the proposed approaches on several datasets, such as accelerometer data collected from wearable sensors and ECG data.
Index Terms:
Time series analysis,Hidden Markov models,Computational modeling,Discrete Fourier transforms,Time measurement,Discrete wavelet transforms,Extraterrestrial measurements,time series classification,Activity recognition,gait recognition,supervised learning,unsupervised learning,wearable computing
Citation:
J. Frank, S. Mannor, J. Pineau, D. Precup, "Time Series Analysis Using Geometric Template Matching," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 35, no. 3, pp. 740-754, March 2013, doi:10.1109/TPAMI.2012.121
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