Using multiple indexes for efficient subsequence matching in time-series databases

TL;DR: This work shows that by using a combination of four novel ideas the authors can search and mine truly massive time series for the first time, and shows that in large datasets they can exactly search under DTW much more quickly than the current state-of-the-art Euclidean distance search algorithms.

TL;DR: In this paper, a new time-series forecasting model based on the flexible neural tree (FNT) is introduced. But the model is not suitable for time series forecasting and it is difficult to select the proper input variables or time-lags for constructing a time series model.

TL;DR: This work shows that by using a combination of four novel ideas the authors can search and mine massive time series for the first time, and demonstrates the following unintuitive fact: in large datasets they can exactly search under Dynamic Time Warping much more quickly than the current state-of-the-art Euclidean distance search algorithms.

TL;DR: In this article, the authors proposed a new subsequence matching method, Dual Match, which exploits duality in constructing windows and significantly improves the performance of the FRM algorithm by storing minimum bounding rectangles rather than individual points representing windows.

TL;DR: This work introduces a novel technique for the exact indexing of Dynamic time warping and proves its vast superiority over all competing approaches in the largest and most comprehensive set of time series indexing experiments ever undertaken.

TL;DR: In this paper, a survey of the available data mining techniques is provided and a comparative study of such techniques is presented, based on a database researcher's point-of-view.

TL;DR: An indexing method for time sequences for processing similarity queries using R * -trees to index the sequences and efficiently answer similarity queries and provides experimental results which show that the method is superior to search based on sequential scanning.

TL;DR: An efficient indexing method to locate 1-dimensional subsequences within a collection of sequences, such that the subsequences match a given (query) pattern within a specified tolerance.