An Empirical Study of Neural Networks for Trend Detection in Time Series

Abstract

Even if trend is probably one of the most intuitive notions in time series dynamics, this notion is usually ambiguous and model dependent. We first cast the trend detection problem into a sequence-to-sequence classification problem. Then, we simulate various dynamics with labelled trends. Using those simulated time-series we build a baseline trend estimator showing good performance on various dynamics. Comparing this baseline estimator with various other trend estimators, we find that some recurrent neural networks structures compare favourably against other estimators including convolutional neural networks. Those sequence-to-sequence trend classifiers could be used as efficient basic blocks to build more complex time series estimators.