Forecasting of Categorical Time Series Using Computing with Words Model

Abstract

This paper addresses the problem of forecasting categorical time series, which have wide application in all areas of activity where judgement and expert judgement are used. One of the main problems in forecasting such series is to account for the linguistic uncertainty that arises in expert judgement, moving from qualitative to quantitative assessment and vice versa. This paper proposes a categorical time series prediction model based on computing with words. A codebook of 32, 15, 11, 9, 8, 7, 6 or 4 words is suggested for comparing categorical time series attributes. The number of words depends on the level of detail of the categorical attributes of the series. Words from codebooks are described using discrete interval type-2 fuzzy sets, which allows for the linguistic uncertainty of the categorical attributes of the series. Based on the proposed model, a fuzzy algorithm for categorical time series forecasting is developed, consisting of five steps: word model definition, fuzzy relationship definition, fuzzy relationship grouping, fuzzy forecasting, result interpretation. The quality of the proposed model is confirmed by three estimated characteristics: mean absolute prediction error; mean square prediction error; mean relative prediction error. © 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).