KENN: Enhancing Deep Neural Networks by Leveraging Knowledge for Time Series Forecasting

TL;DR

KENN is a novel neural network architecture that combines domain knowledge with data-driven learning to improve time series forecasting, especially when limited training data is available.

Contribution

The paper introduces KENN, a knowledge fusion architecture that enhances neural networks by integrating domain knowledge, reducing data dependency, and improving forecasting accuracy.

Findings

KENN outperforms state-of-the-art methods.

KENN achieves better predictions with only 50% of training data.

Knowledge integration enhances model robustness.

Abstract

End-to-end data-driven machine learning methods often have exuberant requirements in terms of quality and quantity of training data which are often impractical to fulfill in real-world applications. This is specifically true in time series domain where problems like disaster prediction, anomaly detection, and demand prediction often do not have a large amount of historical data. Moreover, relying purely on past examples for training can be sub-optimal since in doing so we ignore one very important domain i.e knowledge, which has its own distinct advantages. In this paper, we propose a novel knowledge fusion architecture, Knowledge Enhanced Neural Network (KENN), for time series forecasting that specifically aims towards combining strengths of both knowledge and data domains while mitigating their individual weaknesses. We show that KENN not only reduces data dependency of the overall framework but also improves performance by producing predictions that are better than the ones produced by purely knowledge and data driven domains. We also compare KENN with state-of-the-art forecasting methods and show that predictions produced by KENN are significantly better even when trained on only 50\% of the data.