Modeling Water Cluster Anions

A quantum Drude oscillator model was developed by our group to describe excess electrons interacting with water clusters1. This approach uses quantum Drude-oscillators to account for polarization and dispersion interactions between the excess electron and the water molecules. In the present work, the quantum Drude model£¬combined with a modified Thole-type water model with dipole point polarizability, denoted DPP, is used to investigate the (H2O)7- cluster. Several low-energy isomers were characterized, and the finite-temperature properties of the cluster was investigated by means of parallel tempering Monte Carlo simulations

Forecasting with time series imaging

Feature-based time series representations have attracted substantial attention in a wide range of time series analysis methods. Recently, the use of time series features for forecast 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 extract features from time series automatically becomes crucial in state-of-the-art time series analysis. In this paper, we introduce an automated approach to extract time series features based on time series imaging. We first transform time series into recurrence plots, from which local features can be extracted using computer vision algorithms. The extracted features are used for forecast 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 highly comparable performances with the best methods in the largest forecasting competition dataset (M4) and outperforms the top methods in the Tourism forecasting competition dataset