Time Series Features and Machine Learning Forecasts

Abstract

In this study we combine the results of two independent analyses to position Spanish regions according to both the characteristics of the time series of international tourist arrivals and the accuracy of predictions of arrivals at the regional level. We apply a seasonal-trend decomposition procedure based on non-parametric regression to isolate the different components of the series and calculate the main time series features. Predictions are generated with several machine learning models in a recursive multi-step-ahead forecasting experiment. Finally, we summarize all the information from the two previous experiments using categorical principal component analysis. By overlapping the distribution of the regions and the component loadings of each variable along both dimensions, we observe that entropy and dispersion show an inverse relation with forecast accuracy, but the interactions between the rest of the features and accuracy are heavily dependent on the forecast horizon. On this evidence, we conclude that in order to increase forecast accuracy of tourist arrivals at the regional level, model selection should be region-specific and based on the forecast horizon.

An application of deep learning for exchange rate forecasting

“…The advent of DL has rekindled the interest for NN forecasting. The advances over the past two decades have proven the potential of NNs for time series forecasting, especially in situations where long series are available (Andrawis et al, 2011;Ben Taieb et al, 2012;Claveria et al, 2015Claveria et al, , 2016Claveria et al, , 2017Claveria et al, , 2020Crone et al, 2011;Feng & Zhang, 2014).…”