Novel efficient reservoir computing methodologies for regular and irregular time series classification

Time series is a data structure prevalent in a wide range of fields such as healthcare, finance and meteorology. Analyzing time series data holds the key to gaining insight into our day-to-day observations and among them, time series classification offers the unique opportunity to classify the sequences into their respective categories for the sake of automated detection. To this end, two types of mainstream approaches, recurrent neural networks and distance-based methods, have been commonly employed to address this specific problem. However, the most successful ones such as Long Short-Term Memory networks generally suffer substantially high computational demand, prompting the search for more efficient alternatives to reduce energy costs. Reservoir computing is an instance of recurrent neural networks that is known for its efficiency in processing time series sequences. Therefore, in this article, we will develop two reservoir computing based methods that can effectively deal with time series of different types with minimal computational cost, all while achieving a desirable level of classification accuracy. Mathematics Subject Classification (2020) 34A34 · 62P10 · 68T07