The problem of concept drift has gained a lot of attention in recent years. This aspect is key
in many domains exhibiting non-stationary as well as cyclic patterns and structural breaks …

Many subfields of machine learning share a common stumbling block: evaluation. Advances
in machine learning often evaporate under closer scrutiny or turn out to be less widely …

Recent trends in the Machine Learning (ML) and in particular Deep Learning (DL) domains
have demonstrated that with the availability of massive amounts of time series, ML and DL …

Multivariate time series forecasting focuses on predicting future values based on historical
context. State-of-the-art sequence-to-sequence models rely on neural attention between …

The recent global expansion of dengue has been facilitated by changes in urbanisation,
mobility, and climate. In this work, we project future changes in dengue incidence and case …

ABSTRACT Automated Valuation Models (AVMs) based on Machine Learning (ML)
algorithms are widely used for predicting house prices. While there is consensus in the …

This research paper explores the performance of Machine Learning (ML) algorithms and
techniques that can be used for financial asset price forecasting. The prediction and …

Mistakes in machine learning practice are commonplace, and can result in a loss of
confidence in the findings and products of machine learning. This guide outlines common …

Importance A SARS-CoV-2 vaccine was approved for adolescents aged 12 to 15 years on
May 10, 2021, with approval for younger age groups following thereafter. The population …

Because of high fluctuations of tourism demand, accurate predictions of tourist arrivals are of
high importance for tourism organizations. The study at hand presents an approach to …