I am delighted to serve on the Program Committee of the Twelfth International Conference on Monte Carlo Methods and Application (MCM 2019), to be held in Sydney, Australia, from July 8 to 12, 2019.
I am organizing a special thematic session (of either 3 or 4 talks of 30 minutes each) in Monte Carlo with Applications. If you are interested, please let me know.
I am giving a workshop in Guanghua School of Management at Peking University.
Time: 14:00-17:00 on Nov 31, Dec 7, 14, 21
Venue: Guanghua Building #1, Room 114
Checkout this link for more details.
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 evaluation tool for tasks such as time series classification and forecasting.