Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Explaining Bad Forecasts in Global Time Series Models

Version 1 : Received: 9 August 2021 / Approved: 11 August 2021 / Online: 11 August 2021 (10:32:48 CEST)

A peer-reviewed article of this Preprint also exists.

Rožanec, J.; Trajkova, E.; Kenda, K.; Fortuna, B.; Mladenić, D. Explaining Bad Forecasts in Global Time Series Models. Appl. Sci. 2021, 11, 9243. Rožanec, J.; Trajkova, E.; Kenda, K.; Fortuna, B.; Mladenić, D. Explaining Bad Forecasts in Global Time Series Models. Appl. Sci. 2021, 11, 9243.

Journal reference: Appl. Sci. 2021, 11, 9243
DOI: 10.3390/app11199243

Abstract

While increasing empirical evidence suggests that global time series forecasting models can achieve better forecasting performance than local ones, there is a research void regarding when and why the global models fail to provide a good forecast. This paper uses anomaly detection algorithms and Explainable Artificial Intelligence (XAI) to answer when and why a forecast should not be trusted. To address this issue, a dashboard was built to inform the user regarding (i) the relevance of the features for that particular forecast, (ii) which training samples most likely influenced the forecast outcome, (iii) why the forecast is considered an outlier, and (iv) provide a range of counterfactual examples to understand value changes, in the feature vector or the predicted value, can lead to a different outcome. Moreover, a modular architecture and a methodology were developed to iteratively remove noisy data instances from the train set, to enhance the overall global time series forecasting model performance. Finally, to test the effectiveness of the proposed approach, it was validated on two publicly available real-world datasets.

Keywords

Explainable Artificial Intelligence; XAI; Time Series Forecasting; Global Time Series Models; Machine Learning; Artificial Intelligence

Subject

ENGINEERING, Electrical & Electronic Engineering

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