Abstract Machine learning has been widely adopted for improving building energy efficiency
and flexibility in the past decade owing to the ever-increasing availability of massive building …

Temporal data representing chronological observations of complex systems can be
ubiquitously collected in smart industry, medicine, finance and etc. In the last decade, many …

Accurate and real-time forecasting of the price of oil plays an important role in the world
economy. Research interest in forecasting this type of time series has increased …

While most classical approaches to Granger causality detection assume linear dynamics,
many interactions in real-world applications, like neuroscience and genomics, are inherently …

Having insight into the causal associations in a complex system facilitates decision making,
eg, for medical treatments, urban infrastructure improvements or financial investments. The …

For recurrent neural networks trained on time series with target and exogenous variables, in
addition to accurate prediction, it is also desired to provide interpretable insights into the …

Activity logs collected from wearable devices (eg Apple Watch, Fitbit, etc.) are a promising
source of data to facilitate a wide range of applications such as personalized exercise …

Explanations of time series models are useful for high stakes applications like healthcare but
have received little attention in machine learning literature. We propose FIT, a framework …

Phishing is an attempt to acquire sensitive information from an unsuspecting victim by
malicious means. Recent studies have shown that phishers often use persuasion …

Bitcoin is one of the most prominent decentralized digital cryptocurrencies. Ability to
understand which factors drive the fluctuations of the Bitcoin price and to what extent they …