An empirical study of neural networks for trend detection in time series
This work addresses trend detection in time series, but it is incremental as it builds on existing RNN methods without introducing new paradigms.
The study tackled the problem of detecting trends in noisy time series by empirically evaluating standard recurrent neural networks (RNNs), showing their overall superiority and versatility over other estimators.
Detecting structure in noisy time series is a difficult task. One intuitive feature is the notion of trend. From theoretical hints and using simulated time series, we empirically investigate the efficiency of standard recurrent neural networks (RNNs) to detect trends. We show the overall superiority and versatility of certain standard RNNs structures over various other estimators. These RNNs could be used as basic blocks to build more complex time series trend estimators.