For the last years, time-series mining has become a challenging issue for researchers. An important application lies in most monitoring purposes, which require analyzing large sets of time-series for learning usual patterns. Any deviation from this learned profile is then considered as an unexpected situation. Moreover, complex applications may involve the temporal study of several heterogeneous parameters. In that paper, we propose a method for mining heterogeneous multivariate time-series for learning meaningful patterns. The proposed approach allows for mixed time-series -- containing both pattern and non-pattern data -- such as for imprecise matches, outliers, stretching and global translating of patterns instances in time. We present the early results of our approach in the context of monitoring the health status of a person at home. The purpose is to build a behavioral profile of a person by analyzing the time variations of several quantitative or qualitative parameters recorded through a provision of sensors installed in the home.
@cite_28: Several important time series data mining problems reduce to the core task of finding approximately repeated subsequences in a longer time series. In an earlier work, we formalized the idea of approximately repeated subsequences by introducing the notion of time series motifs. Two limitations of this work were the poor scalability of the motif discovery algorithm, and the inability to discover motifs in the presence of noise.Here we address these limitations by introducing a novel algorithm inspired by recent advances in the problem of pattern discovery in biosequences. Our algorithm is probabilistic in nature, but as we show empirically and theoretically, it can find time series motifs with very high probability even in the presence of noise or "don't care" symbols. Not only is the algorithm fast, but it is an anytime algorithm, producing likely candidate motifs almost immediately, and gradually improving the quality of results over time.
@cite_19: Pevzner and Sze [23] considered a precise version of the motif discovery problem and simultaneously issued an algorithmic challenge: find a motif M of length 15, where each planted instance differs from M in 4 positions. Whereas previous algorithms all failed to solve this (15,4)-motif problem. Pevzner and Sze introduced algorithms that succeeded. However, their algorithms failed to solve the considerably more difficult (14,4)-, (16,5)-, and (18,6)-motif problems. We introduce a novel motif discovery algorithm based on the use of random projections of the input's substrings. Experiments on simulated data demonstrate that this algorithm performs better than existing algorithms and, in particular, typically solves the difficult (14,4)-, (16,5)-, and (18,6)-motif problems quite efficiently. A probabilistic estimate shows that the small values of d for which the algorithm fails to recover the planted (l, d)-motif are in all likelihood inherently impossible to solve. We also present experimental results on realistic biological data by identifying ribosome binding sites in prokaryotes as well as a number of known transcriptional regulatory motifs in eukaryotes.
@cite_34: 
@cite_13: This paper proposes a new scheme for unsupervised multi-temporal sequence pattern extraction. The main idea of the scheme is iterative coarse to fine data examination. We decompose a pattern into ambiguous subpatterns and distinguishable sub-patterns (DSP). In each iteration, we coarsely examine the training temporal signal sequence by training an Elman neural network. The trained Elman network is used to select the DSP candidate set. Then, we look at the training signals around the DSPs and use maximum likelihood criteria to expand them into whole patterns. We cut out the new found patterns from the training signal sequence and repeat the whole procedure until no more new patterns are found. The experimental result shows this method promising.
The techniques used for time-series mining vary according to the application, regarding the characteristics of both the temporal sequences under study and the expected patterns: degree of variability in the values, allowed transformations between instances of a same pattern, possible stretching in time. For instance, Hong have experimented training recurrent neural networks for an unsupervised extraction of multi-temporal sequence patterns @cite_13 . However, this method suffers from noisy data. The use of finite state machines @cite_34 may give out good results, which may however dramatically decrease as the dimensionality increases - that is the number of states. Chiu @cite_28 have implemented in the context of times-series motifs extraction an efficient algorithm based on random projections initially proposed by Buhler and Tompa to find motifs in nucleotide sequences @cite_19 . Although they only deal with one-dimensional time-series and then do not address the issue of heterogeneous multivariate time-series, this algorithm is actually interesting because of the rapid extraction of approximate results and the efficiency even in the presence of noise or don't care'' symbols. However this method, as implemented in @cite_28 , does not allow for stretching in time between motifs instances.