This flow can be quantified and compared by analyzing the text using natural language processing techniques. This study uses word embedding models to generate time series for novels and then compare the resulting series using dynamic time warping to find similarities. Considering time series analysis rather than a pure statistical one can capture the flow of the works and thus the similarities ...

Continuously monitoring through time the correlation/distance of multiple data streams is of interest in a variety of applications, including financial analysis, video surveillance, and mining of biological data. However, distance measures commonly adopted for comparing time series, such as Euclidean and Dynamic Time Warping (DTW ), either are known to be inaccurate or are too time-consuming to...

This paper presents a new approach for code similarity on High Level programs. Our technique is based on Fast Dynamic Time Warping, that builds a warp path or points relation with local restrictions. The source code is represented into Time Series using the operators inside programming languages that makes possible the comparison. This makes possible subsequence detection that represent similar...

Time series classification in the dissimilarity space combines the advantages of the dynamic time warping and the rich mathematical structure of Euclidean spaces. We applied dimension reduction using PCA followed by support vector learning on dissimilarity representations to 43 UCR datasets. Results indicate that time series classification in dissimilarity space has potential to complement the ...

many of the video processing algorithms cannot be implemented in real time on general computers, due to their computational complexity. for an efficient implementation, a custom hardware that can be reconfigured for the algorithm, is used. in this paper a new acceleration hardware based on fpga elements is proposed. this hardware can be adapted with the processing algorithm through fpga design ...