Time series are the primary data type used to record dynamic system measurements and
generated in great volume by both physical sensors and online processes (virtual sensors) …

In the era of big data, there has been a surge in the availability of data containing rich spatial
and temporal information, offering valuable insights into dynamic systems and processes for …

Spatiotemporal predictive learning is a deep learning method that generates future frames
from historical frames in a self-supervised manner. Existing studies face the challenges in …

Using search engine data (SED) to forecast tourist flow is essential for management and
security warnings at tourist attractions. Existing prediction models cannot effectively handle …

Forecasting-based method is one of prevalent unsupervised time series anomaly detection
approaches. Currently, large portions of existing forecasting-based methods are devoted to …

Temporal graph representation learning aims to develop low-dimensional embeddings for
nodes in a graph that can effectively capture their structural and temporal properties. Prior …

Purpose The aim of this paper is to provide a narrative review of previous research on
tourism demand modelling and forecasting and potential future developments …

Deep language models have become increasingly prominent in the field of machine
learning. This thesis explores the potential of deep language models for text representation …

Tourism prediction plays a crucial role in the vibrant development of the tourism industry. As
the complexity of tourism data continues to increase, the potential features contained within …