FRANS: Automatic Feature Extraction for Time Series Forecasting
This addresses the challenge of reducing manual effort in time series forecasting for practitioners, though it is incremental as it builds on existing meta-learning frameworks.
The paper tackles the problem of automating feature extraction for time series forecasting by introducing FRANS, a method that eliminates the need for domain knowledge and handcrafted features, resulting in improved accuracy in most situations and features generated orders of magnitude faster than statistical methods.
Feature extraction methods help in dimensionality reduction and capture relevant information. In time series forecasting (TSF), features can be used as auxiliary information to achieve better accuracy. Traditionally, features used in TSF are handcrafted, which requires domain knowledge and significant data-engineering work. In this research, we first introduce a notion of static and dynamic features, which then enables us to develop our autonomous Feature Retrieving Autoregressive Network for Static features (FRANS) that does not require domain knowledge. The method is based on a CNN classifier that is trained to create for each series a collective and unique class representation either from parts of the series or, if class labels are available, from a set of series of the same class. It allows to discriminate series with similar behaviour but from different classes and makes the features extracted from the classifier to be maximally discriminatory. We explore the interpretability of our features, and evaluate the prediction capabilities of the method within the forecasting meta-learning environment FFORMA. Our results show that our features lead to improvement in accuracy in most situations. Once trained our approach creates features orders of magnitude faster than statistical methods.