In the field of new energy vehicles, lithium-ion batteries have become an inescapable
energy storage device. However, they still face significant challenges in practical use due to …

Recently, there has been a surge of Transformer-based solutions for the long-term time
series forecasting (LTSF) task. Despite the growing performance over the past few years, we …

Heterogeneous tabular data are the most commonly used form of data and are essential for
numerous critical and computationally demanding applications. On homogeneous datasets …

The multi-layer structures of Deep Learning facilitate the processing of higher-level
abstractions from data, thus leading to improved generalization and widespread …

Time series outlier detection has been extensively studied with many advanced algorithms
proposed in the past decade. Despite these efforts, very few studies have investigated how …

Multivariate time series data comprises various channels of variables. The multivariate
forecasting models need to capture the relationship between the channels to accurately …

Time series anomaly detection is critical for a wide range of applications. It aims to identify
deviant samples from the normal sample distribution in time series. The most fundamental …

Random forest (RF) is one of the most popular machine learning (ML) models used for both
classification and regression problems. As an ensemble model, it demonstrates high …

Applying computational statistics or machine learning methods to data is a key component of
many scientific studies, in any field, but alone might not be sufficient to generate robust and …

The progressive growth in demand and requirements for bearing problem diagnostics in the
operating segment of trains has resulted from an increase in train speed and the …