Publikation
DeepEX: Bridging the Gap Between Knowledge and Data Driven Techniques for Time Series Forecasting
Muhammad Ali Chatta; Muhammad Shoaib Ahmed Siddiqui; Moshin Munir; Ludger van Elst; Imran Malik; Andreas Dengel; Sheraz Ahmed
In: The 28th International Conference on Artificial Neural Networks. International Conference on Artificial Neural Networks (ICANN-2019), September 17-19, Munich, Germany, IEEE, 9/2019.
Zusammenfassung
Artificial Intelligence (AI) can roughly be categorized into two streams, knowledge driven and data driven both of which have their own advantages. Incorporating knowledge into Deep Neural Networks (DNN), that are purely data driven, can potentially improve the overall performance of the system. This paper presents such a fusion scheme, DeepEX, that combines these seemingly parallel streams of AI, for multistep time-series forecasting problems. DeepEX achieves this in a way that merges best of both worlds along with a reduction in the amount of data required to train these models. This direction has been explored in the past for single step forecasting by opting for a residual learning scheme. We analyze the shortcomings of this simple residual learning scheme and enable DeepEX to not only avoid these shortcomings but also scale to multi-step prediction problems. DeepEX is tested on two commonly used time series forecasting datasets, CIF2016 and NN5, where it achieves competitive results even when trained on a reduced set of training examples. Incorporating external knowledge to reduce network’s reliance on large amount of accurately labeled data will prove to be extremely effective in training of neural networks for real-world applications where the dataset sizes are small and labeling is expensive.