Models of performance of time series forecasters

• We propose a novel set of time series features.
• We test our previous difficulty indicators on the domain of time series forecasting.
• The features proposed herein are very general being applicable to other domains.
• The algorithm portfolios proposed outperforms the forecasters tested.

One of the first steps when approaching any machine learning task is to select, among all the available procedures, which one is the most adequate to solve a particular problem; in automated problem solving this is known as the algorithm selection problem. Of course, this problem is also present in the field of time series forecasting, there, one needs to select the forecaster that makes the most accurate predictions. Generally, this selection task is manually performed by analyzing the characteristics of the time series, thus relying on the expertise that one has on the available forecasters. In this paper, we propose an automatic procedure to choose a forecaster given a set of candidates, i.e., to solve the algorithm selection problem on this domain. To do so, we follow two paths. Firstly, we propose to model the performance of the forecasters using a linear combination of features that were previously used to assess the problem difficulty of evolutionary algorithms, together with a set of features we propose in this paper. Then, this model is used to predict the performance of the forecasters and based on these predictions the forecaster is selected. Our second approach is to treat this algorithm selection process as a classification task where the descriptors of each time series are the proposed features. To show the capabilities of our approach, we test the forecasters on the time series of the M1 and M3 time series competitions and used three different forecasters. In all the cases tested, our proposals outperform the performance of the three forecasters indicating the viability of our approach.