New Paper: GRATIS: GeneRAting TIme Series with diverse and controllable characteristics

The explosion of time series data in recent years has brought a flourish of new time series analysis methods, for forecasting, clustering, classification and other tasks. The evaluation of these new methods requires a diverse collection of time series data to enable reliable comparisons against alternative approaches. We propose the use of mixture autoregressive (MAR) models to generate collections of time series with diverse features. We simulate sets of time series using MAR models and investigate the diversity and coverage of the simulated time series in a feature space. An efficient method is also proposed for generating new time series with controllable features by tuning the parameters of the MAR models. The simulated data based on our method can be used as an evaluation tool for tasks such as time series classification and forecasting.

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By Feng Li

Dr. Feng Li is an Associate Professor of Statistics in the School of Statistics and Mathematics at Central University of Finance and Economics in Beijing, China. Feng obtained his Ph.D. degree in Statistics from Stockholm University, Sweden in 2013. His research interests include Bayesian computation, econometrics and forecasting, and distributed learning. His recent research output appeared in statistics and forecasting journals such as the International Journal of Forecasting and Statistical Analysis and Data Mining, AI journals such as Expert Systems with Applications, and medical journals such as BMJ Open.

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