Insects play significant role in the human life. And insects pollinate major food crops consumed in the world. Insect pests consume and destroy major crops in the world. Hence to have control over the disease and pests, researches are going on in the area of entomology using chemical, biological and mechanical approaches. The data relevant to the flying insects often changes over time, and clas...

Data mining research into time series classification (TSC) has focussed on alternative distance measures for nearest neighbour classifiers. It is standard practice to use 1-NN with Euclidean or dynamic time warping (DTW) distance as a straw man for comparison. As part of a wider investigation into elastic distance measures for TSC [1], we perform a series of experiments to test whether this sta...

Recently DTW (dynamic time warping) has been recognized as the most robust distance function to measure the similarity between two time series, and this fact has spawned a flurry of research on this topic. Most indexing methods proposed for DTW are based on the R-tree structure. Because of high dimensionality and loose lower bounds for time warping distance, the pruning power of these tree stru...

We present two heuristics for speeding up a time series alignment algorithm that is related to dynamic time warping (DTW). In previous work, we developed our multisegment alignment algorithm to answer similarity queries for toxicogenomic time-series data. Our multisegment algorithm returns more accurate alignments than DTW at the cost of time complexity; the multisegment algorithm is O(n(5)) wh...

This extended abstract describes our real-time music tracking system, which was submitted to the MIREX 2010 Score Following task. Our system is based on an on-line version of the well-known Dynamic Time Warping (DTW) algorithm and includes some extensions to improve both the precision and the robustness of the alignment (e.g. a tempo model and the ability to reconsider past decisions).

Dynamic Time Warping (DTW) is a time series distance measure that allows non-linear alignments between series. Constraints on the in form of windows and weights have been introduced because unconstrained DTW too permissive its alignments. However, windowing introduces crude step function, allowing flexibility within window, none beyond it. While not entailing multiplicative weight relative to d...

This introduction to the R package dtw is a (slightly) modified version of Giorgino (2009), published in the Journal of Statistical Software. Dynamic time warping is a popular technique for comparing time series, providing both a distance measure that is insensitive to local compression and stretches and the warping which optimally deforms one of the two input series onto the other. A variety o...

Time-series classification has gained wide attention within the Machine Learning community, due to its large range of applicability varying from medical diagnosis, financial markets, up to shape and trajectory classification. The current state-of-art methods applied in timeseries classification rely on detecting similar instances through neighboring algorithms. Dynamic Time Warping (DTW) is a s...

Alignment of time series is an important problem to solve in many scientific disciplines. In particular, temporal alignment of two or more subjects performing similar activities is a challenging problem due to the large temporal scale difference between human actions as well as the inter/intra subject variability. In this paper we present canonical time warping (CTW), an extension of canonical ...

In this paper, we propose an innovative averaging of a set of time-series based on the Dynamic Time Warping (DTW). The DTW is widely used in data mining since it provides not only a similarity measure, but also a temporal alignment of time-series. However, its use is often restricted to the case of a pair of signals. In this paper, we propose to extend its application to a set of signals by pro...