English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

End-to-end data-driven machine learning methods often have exuberantrequirements in terms of quality and quantity of training data which are oftenimpractical to fulfill in real-world applications. This is specifically true intime 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 sincein doing so we ignore one very important domain i.e knowledge, which has itsown distinct advantages. In this paper, we propose a novel knowledge fusionarchitecture, Knowledge Enhanced Neural Network (KENN), for time seriesforecasting that specifically aims towards combining strengths of bothknowledge and data domains while mitigating their individual weaknesses. Weshow that KENN not only reduces data dependency of the overall framework butalso improves performance by producing predictions that are better than theones produced by purely knowledge and data driven domains. We also compare KENNwith state-of-the-art forecasting methods and show that predictions produced byKENN are significantly better even when trained on only 50\% of the data.

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