Semantics-Aware Patch Encoding and Hierarchical Dependency Modeling for Long-Term Time Series Forecasting
Open Access
- 3 August 2025
- conference paper
- Published by Association for Computing Machinery (ACM)
- p. 2269-2280
Abstract
No abstract availableThis publication has 9 references indexed in Scilit:
- Intrinsic Explainable Artificial Intelligence Using Trainable Spatial Weights on Numerical Weather Predictions2024
- HDMixer: Hierarchical Dependency with Extendable Patch for Multivariate Time Series ForecastingProceedings of the AAAI Conference on Artificial Intelligence, 2024
- Are Transformers Effective for Time Series Forecasting?Proceedings of the AAAI Conference on Artificial Intelligence, 2023
- Multi-Scale Adaptive Graph Neural Network for Multivariate Time Series ForecastingIEEE Transactions on Knowledge and Data Engineering, 2023
- Time-series clustering and forecasting household electricity demand using smart meter dataEnergy Reports, 2023
- Predicting Household Electric Power Consumption Using Multi-step Time Series with Convolutional LSTMBig Data Research, 2022
- METROProceedings of the VLDB Endowment, 2021
- Financial time series forecasting with deep learning : A systematic literature review: 2005–2019Applied Soft Computing, 2020
- Adaptive Mixtures of Local ExpertsNeural Computation, 1991