DeepEX: Bridging the Gap Between Knowledge and Data Driven Techniques for Time Series Forecasting

DeepLSF: Fusing Knowledge and Data for Time Series Forecasting

“…The use of statistical methods as a source of knowledge in modeling time series is not uncommon. Chattha et al (2019a) [22] proposed a residual learning scheme, called Knowledge Integrated Neural Network(KINN), where they incorporated expert knowledge in the form of prediction in the network by adding it to the network's output. Despite its advantages, the approach cannot be directly scaled to multi-step predictions.…”

“…This was primarily because KINN was unable to model time series that had a strong trend present in the past sequences. They improved upon KINN in DeepEx (2019) [23] where they used a separate predictor to model trends in the data. Similarly, [24] also utilized the power of statistical models to enhance the performance of their DNN which was ultimately used for the task of anomaly detection.…”