Overcoming Upper and Lower Bound Limitations of Input in Cortical Learning Algorithm

Abstract

Time series forecasting is one of the essential information technologies for efficientanomaly detection and decision-making. Since most time series data contain various non-stationary features that are difficult to eliminate completely, it is important to learn continuously in an online manner. The Cortical Learning Algorithm (CLA), which mimics the human neocortex, is suitable for online learning and time series prediction. In this study, to make CLA more adapted to online learning, we propose a method to handle inputs that exceed predefined upper and lower input bounds. Experimental results show that not only can the upper and lower input constraints be eliminated, but also the prediction accuracy can be improved by maintaining an appropriate level of input representation granularity for the learner.