Modeling multi-variate time-series (MVTS) data is a long-standing research subject and has found wide applications. Recently, there surge of interest in modeling spatial relations between variables as graphs, i.e., first learning one static graph for each dataset then exploiting the structure via neural networks. However, may differ substantially across samples, building all samples inherently ...

The era of modern financial data modeling seeks machine learning techniques which are suitable for noisy and non-stationary big data. We demonstrate how a general class exponential smoothed recurrent neural networks ( α -RNNs) well suited to dynamical systems arising in applications such as high frequency algorithmic trading. Application exponentially RNNs minute level Bitcoin prices CME future...

Abstract Road traffic accidents (RTA) are commonly encountered incidents that can cause injuries, death, and property damage to members of society. Ethiopia is one the highest incident rates road accidents. Report Transport Communication from 2012 2014, shows an increment in number Ethiopia. Amhara region accounted for 27.3% total accident-related deaths during year 2008/9, which share among al...

Improving time series forecastingaccuracy is an important yet often difficult task.Both theoretical and empirical findings haveindicated that integration of several models is an effectiveway to improve predictive performance, especiallywhen the models in combination are quite different. In this paper,a model of the hybrid artificial neural networks andfuzzy model is proposed for time series for...