Interpretation-aware Cognitive Map construction for time series modeling.

We raise a few issues regarding time series modeling using Cognitive Maps, which is an example of a qualitative rather than a purely quantitative approach. Methods that operate at the level of concepts instead of numerical values are a worthy alternative for time series processing thanks to certain desirable properties such as abstraction and ease of interpretation. Those features are unavailable in classical time series modeling algorithms, where the main concern is numerical accuracy of a prediction expressed as a series of numerical values. In this work, we argue that it is desirable for Cognitive Map-based models to be simplified, ideally to the point where their descriptiveness and numerical accuracy is not compromised. In particular, we show experimentally that both the size of the map and the size of the set of possible weight values can be minimized without increasing the error rate.