Learning regularity in an economic time-series for structure prediction

Abstract Although an economic time-series has an apparently random fluctuation over time, there exists certain regularity in the functional behavior of the series. This paper attempts to identify the regularly occurring structures in an economic time-series with an aim to represent the series as a specific sequence of such structures for forecasting applications. The applications include prediction of the most probable structure with its expected duration, along with predicted values lying thereon. Representation of a time-series by a set of regularly recurring structures is undertaken by invoking three main steps: (i) non-uniform length segmentation of the series, (ii) identification of the recurrent patterns by clustering of the generated segments, and (iii) representing the sequence of regular structures using a specially designed automaton. The automaton is used here to both encode the sequence of structures representing the time-series and also to act as an inference engine for stochastic forecasting about the time-series. Experiments undertaken on large (28 years’) daily economic time-series data sets confirm the success in automated structure prediction with an average prediction accuracy of 88.05%, average precision of 91.24% and average recall of 93.42%.