Telecommunication companies collect a deluge of subscriber data without retrieving
substantial information. Exploratory analysis of this type of data will facilitate the prediction of …

Real-world applications often involve irregular time series, for which the time intervals
between successive observations are non-uniform. Irregularity across multiple features in a …

Deep learning has contributed remarkably to the advancement of time series analysis. Still,
deep models can encounter performance bottlenecks in real-world small-sample scenarios …

Recently network-based method for forecasting time series has become a hot research
topic. Although some methods have been recognized for their prediction performance, how …

Substantial recent progress in machine learning has been driven by advances in sequence
models, which form the backbone of deep learning models that have achieved widespread …

We argue that time series analysis is fundamentally different in nature to either vision or
natural language processing with respect to the forms of meaningful self-supervised …

Time series research has gathered lots of interests in the last decade, especially for Time
Series Classification (TSC) and Time Series Forecasting (TSF). Research in TSC has greatly …

Time-series data exists in every corner of real-world systems and services, ranging from
satellites in the sky to wearable devices on human bodies. Learning representations by …

Seismograms contain multiple sources of seismic waves, from distinct transient signals such
as earthquakes to continuous ambient seismic vibrations such as microseism. Ambient …

Introduction: This exploratory study aims to participate in the development of the VR
framework by focusing on the issue of cybersickness. The main objective is to explore the …