Adaptive and innovative application of classical data mining principles and techniques in
time series analysis has resulted in development of a concept known as time series data …

Owing to the central role in electricity market operation, researchers have long sought to
investigate the price responsiveness of both electricity supply and consumption sides. From …

Anomaly detection is the problem of finding patterns in data that do not conform to an a priori
expected behavior. This is related to the problem in which some samples are distant, in …

We introduce a method to discover optimal local patterns, which concisely describe the main
trends in a time series. Our approach examines the time series at multiple time scales (ie …

The Fréchet distance is a popular distance measure for curves. We study the problem of
clustering time series under the Fréchet distance. In particular, we give (1+∊)-approximation …

This study addresses the development of the English third-person singular present tense
suffix from an interdental fricative (giveth) to an alveolar fricative (gives). Based on the …

We address the problem of capturing and tracking local correlations among time evolving
time series. Our approach is based on comparing the local auto-covariance matrices (via …

We propose an embedding-based framework for subsequence matching in time-series
databases that improves the efficiency of processing subsequence matching queries under …

Regression analysis is a standard supervised machine learning method used to model an
outcome variable in terms of a set of predictor variables. In most real-world applications the …

Numeric time series is a class of data consisting of chronologically ordered observations
represented by numeric values. Much of the data in various domains, such as financial …