The transition to electric vehicles (EVs) and the increased reliance on renewable energy
sources necessitate significant advancements in electrochemical energy storage systems …

Effective imputation is a crucial preprocessing step for time series analysis. Despite the
development of numerous deep learning algorithms for time series imputation, the …

Diffusion models have demonstrated competitive performance in missing data imputation
(MDI) task. However, directly applying diffusion models to MDI produces suboptimal …

With recent advances in building foundation models for texts and video data, there is a surge
of interest in foundation models for time series. A family of models have been developed …

Effective Point-of-Interest (POI) recommendation systems play a pivotal role in modern
location-aware applications and human mobility, facilitating customized suggestions for …

In recent years, the field of electroencephalography (EEG) analysis has witnessed
remarkable advancements, driven by the integration of machine learning and artificial …

In real-world scenarios like traffic and energy, massive time-series data with missing values
and noises are widely observed, even sampled irregularly. While many imputation methods …

In this study, we explore the impact of different masking strategies on time series imputation
models. We evaluate the effects of pre-masking versus in-mini-batch masking, normalization …

Anomaly detection in time series analysis is a pivotal task, yet it poses the challenge of
discerning normal and abnormal patterns in label-deficient scenarios. While prior studies …

The urban environment is characterized by complex spatio-temporal dynamics arising from
diverse human activities and interactions. Effectively modeling these dynamics is essential …