Addressing data dependency in neural networks: introducing the Knowledge Enhanced Neural Network (KENN) for time series forecasting +

Muhammad Ali Chattha, Muhammad Imran Malik, Andreas Dengel, Sheraz Ahmed

Abstract: Purely data-driven deep learning methods often require impractical amounts of high-quality data, which is one of their major weaknesses. This particularly impacts their performance in time series domains, that intrinsically have scarcity of input features. Furthermore, deep learning methods lack the ability to incorporate explicitly defined human knowledge, which can be crucial for finding effective solutions. To address these challenges, we propose a novel fusion framework, Knowledge Enhanced Neural Network (KENN), for time series forecasting. KENN combines knowledge- and data-driven approaches in a novel residual fusion scheme, where information in knowledge and data is combined in a complementary manner. We evaluate KENN in a variety of constrained settings with limited data and inaccurate knowledge models. Even when utilizing only 10% of the data for training, KENN outperforms underlying DNN trained on the complete training set. KENN specifically alleviates data and accuracy constraints of the constituent data and knowledge driven domains while, simultaneously, improving the overall accuracy. We also compare KENN with recent State-of-the-Art (SotA) methods on 5 real-world forecasting datasets. KENN outperforms SotA by an average of 42.2%, when utilizing complete training set, and by 39.7%, when utilizing only 50% of the training set. A fusion framework that reduces dependency of DNN on large datasets and enables harnessing benefits of knowledge driven systems will prove useful in many real-world applications.