Time-series clustering remains a challenge in data mining. Although novel deep-learning-
based representation learning integrated with deep clustering methods have considerably …

The advancements in deep learning methods have presented new opportunities and
challenges for predicting land surface variables (LSVs) due to their similarity with computer …

Impressive advancements have been achieved in utilizing information granulation for
solving long-term time series prediction problems. However, most state-of-the-art methods …

This study aims to analyze and predict furniture store sales using the Long Short-Term
Memory (LSTM) method, focusing on time series datasets from 2014 to 2017. The LSTM …

. Most real-world variables are multivariate time series influenced by past values and
explanatory factors. Consequently, predicting these time series data using artificial …

The increase in global trade, the impact of COVID-19, and the tightening of environmental
and safety regulations have brought significant changes to the maritime transportation …

This paper proposes an innovative approach by integrating deep learning technology,
specifically employing the GRU recurrent neural network model based on the Seagull …