Classification of Univariate Time Series via Temporal Abstraction and Deep Learning

Many time series classification algorithms have been proposed, including deep neural networks based, which so far focused mainly on improving model architectures rather than on data pre-processing. Generalization is crucial in time series classification and it can be achieved by abstracting the data. Data abstraction may also be useful to avoid handling challenges with error measurements, missing values, and irregular sampling. We propose transforming the raw time series into a symbolic time series representation, using a method known as temporal abstraction, before feeding it to the deep neural networks. This transformation can greatly enhance generalization and may potentially improve classification performance. In particular, we investigate the effectiveness of temporal abstraction when combined with convolution-based sequence models or recurrent neural networks. The methods were evaluated on 128 univariate datasets. Our evaluation shows that even when using equal frequency discretization, a relatively simple method, outperforms most state-of-the-art deep neural networks' performance for univariate time series classification when fed by raw time series.