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) …

Numerous deep learning architectures have been developed to accommodate the diversity
of time-series datasets across different domains. In this article, we survey common encoder …

Extending the forecasting time is a critical demand for real applications, such as extreme
weather early warning and long-term energy consumption planning. This paper studies the …

Recently, Transformer-based methods have achieved surprising performance in the field of
long-term series forecasting, but the attention mechanism for computing global correlations …

The price of electricity on the European market is very volatile. This is due both to its mode of
production by different sources, each with its own constraints (volume of production …

Abstract Recurrent Neural Networks (RNNs) have become competitive forecasting methods,
as most notably shown in the winning method of the recent M4 competition. However …

Time series forecasting is an important problem across many domains, including predictions
of solar plant energy output, electricity consumption, and traffic jam situation. In this paper …

Irregular time series data is becoming increasingly prevalent with the growth of multi-sensor
systems as well as the continued use of unstructured manual data recording mechanisms …

Abstract State-of-health (SOH) estimation of lithium-ion batteries is crucial for ensuring the
reliability and safety of battery operation while keeping maintenance and service costs down …

Crop yield prediction is extremely challenging due to its dependence on multiple factors
such as crop genotype, environmental factors, management practices, and their interactions …