The Dynamic Time Warping (DTW) distance measure is a technique that has long been known in speech recognition community. It allows a non-linear mapping of one signal to another by minimizing the distance between the two. A decade ago, DTW was introduced into Data Mining community as a utility for various tasks for time series problems including classification, clustering, and anomaly detection....

In this paper, we present an automatic approach for aligning speech signals to corresponding text documents. For this sake, we propose to first use text-to-speech synthesis (TTS) to obtain a speech signal from the textual representation. Subsequently, both speech signals are transformed to sequences of audio features which are then time-aligned using a variant of greedy dynamic time-warping (DT...

With the rapid spread of built-in GPS handheld smart devices, the trajectory data from GPS sensors has grown explosively. Trajectory data has spatio-temporal characteristics and rich information. Using trajectory data processing techniques can mine the patterns of human activities and the moving patterns of vehicles in the intelligent transportation systems. A trajectory similarity measure is o...

Ekspor non migas merupakan ekspor barang yang bukan berupa minyak dan gas. Tidak semua daerah di Indonesia memiliki potensi sama untuk melakukan kegiatan sehingga setiap nilai berbeda-beda. Oleh karena itu dilakukan analisis pengelompokkan provinsi berdasarkan tahun 2016 – 2020 menggunakan cluster time series dengan metode hierarki clustering agglomerative diantaranya complete lingke yaitu jara...

Many applications generate and consume temporal data and retrieval of time series is a key processing step in many application domains. Dynamic time warping (DTW) distance between time series of size N and M is computed relying on a dynamic programming approach which creates and fills an N × M grid to search for an optimal warp path. Since this can be costly, various heuristics have been propos...

Multi-dimensional time series is playing an increasingly important role in the “big data” era, one noticeable representative being the pervasive trajectory data.Numerous applications ofmulti-dimensional time series all require to find similar time series of a given one, and regarding this purpose, Dynamic Time Warping (DTW) is the most widely used distance measure. Due to the high computation o...

Medical images in biomedical documents tend to be complex by nature and often contain several regions that are annotated using arrows. Arrowhead detection is a critical precursor to regionof-interest (ROI) labeling and image content analysis. To detect arrowheads, images are first binarized using fuzzy binarization technique to segment a set of candidates based on connected component principle....

Dynamic Time Warping (DTW) is a popular similarity measure for aligning and comparing time series. Due to DTW's high computation time, lower bounds are often employed screen poor matches. Many alternative have been proposed, providing range of different trade-offs between tightness computational efficiency. LB Keogh provides useful trade-off in many applications. Two recent bounds, Improved Enh...

An original task of structuring and labeling large television streams is tackled in this paper. Emphasis is put on simple and efficient methods to detect precise boundaries of programs. These programs are further analysed and labeled with information coming from a standard television program guide using an improved Dynamic Time Warping algorithm (DTW) and a manually labeled reference video data...