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 Methods for Large Dependent Data. If you are interested in contributing a talk, please let me know.
- Clara Grazian, University of Oxford
- Ruben Loaiza-Maya, Monash University
- Yanfei Kang, Beihang University
- Feng Li, Central University of Finance and Economics
Our Chinese translation for the forecasting textbook Forecasting: principles and practice by Rob J Hyndman and George Athanasopoulos is now available online.
The Chinese translation was produced by a team led by Professor Yanfei Kang (Beihang University) and Professor Feng Li (Central University of Finance and Economics). The following students were also involved: Cheng Fan, Liu Yu, Long Xiaoyu, Wang Xiaoqian, Zeng Jiayue, Zhang Bohan, and Zhu Shuaidong.
Authors: Xixi Li, Yanfei Kang and Feng Li
Abstract: Feature-based time series representation has attracted substantial attention in a wide range of time series analysis methods. Recently, the use of time series features for forecast model selection and model averaging has been an emerging research focus in the forecasting community. Nonetheless, most of the existing approaches depend on the manual choice of an appropriate set of features. Exploiting machine learning methods to automatically extract features from time series becomes crucially important in the state-of-the-art time series analysis. In this paper, we introduce an automated approach to extract time series features based on images. Time series are first transformed into recurrence images, from which local features can be extracted using computer vision algorithms. The extracted features are used for forecast model selection and model averaging. Our experiments show that forecasting based on automatically extracted features, with less human intervention and a more comprehensive view of the raw time series data, yields comparable performances with the top best methods proposed in the largest forecasting competition M4.
Links: Working Paper