Results 11 to 20 of about 3,518,669 (281)
Hierarchical attention network for multivariate time series long-term forecasting. [PDF]
Appl Intell (Dordr), 2023 Multivariate time series long-term forecasting has always been the subject of research in various fields such as economics, finance, and traffic. In recent years, attention-based recurrent neural networks (RNNs) have received attention due to their ...
Bi H, Lu L, Meng Y.
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Enhancing long-term forecasting: Learning from COVID-19 models. [PDF]
PLoS Comput Biol, 2022 While much effort has gone into building predictive models of the COVID-19 pandemic, some have argued that early exponential growth combined with the stochastic nature of epidemics make the long-term prediction of contagion trajectories impossible.
Rahmandad H, Xu R, Ghaffarzadegan N.
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Long-Term Solar Irradiance Forecasting [PDF]
Problems of the Regional Energetics, 2020 The past decade has been characterized by considerable increase of the penetration level of so-lar photovoltaic systems in energy systems throughout the world. At the same time, solar irradi-ance has an intermittent nature.
Braga D., Chicco G., Golovanov N.
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TimeMachine: A Time Series is Worth 4 Mambas for Long-Term Forecasting. [PDF]
ECAI 2024 (2024)Long-term time-series forecasting remains challenging due to the difficulty in capturing long-term dependencies, achieving linear scalability, and maintaining computational efficiency. We introduce TimeMachine, an innovative model that leverages Mamba, a
Ahamed MA, Cheng Q.
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HUTFormer: Hierarchical U-Net transformer for long-term traffic forecasting
Communications in Transportation Research, 2023 Traffic forecasting, which aims to predict traffic conditions based on historical observations, has been an enduring research topic and is widely recognized as an essential component of intelligent transportation.
Zezhi Shao +9 more
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Hybrid linear time series approach for long term forecasting of crop yield
The Indian Journal of Agricultural Sciences, 2018 Long term forecasting of crop production is required to establish long term vision, say by 2025, to meet growing demand of population at that point of time. Existing univariate linear time series ARIMA approach is valid for short term forecast only.
WASI ALAM +6 more
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xLSTMTime: Long-Term Time Series Forecasting with xLSTM
AIIn recent years, transformer-based models have gained prominence in multivariate long-term time series forecasting (LTSF), demonstrating significant advancements despite facing challenges such as high computational demands, difficulty in capturing temporal dynamics, and managing long-term dependencies.
Musleh Alharthi, Ausif Mahmood
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A data driven model based approach for medium-to-long-term electricity price forecasting in power markets [PDF]
Scientific ReportsAccurate medium-to-long-term electricity price forecasting constitutes a critical prerequisite for electricity market participants to formulate optimal bidding strategies and mitigate energy procurement expenditures.
Jun Hu +3 more
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A Time Series is Worth 64 Words: Long-term Forecasting with Transformers [PDF]
International Conference on Learning Representations, 2022 We propose an efficient design of Transformer-based models for multivariate time series forecasting and self-supervised representation learning. It is based on two key components: (i) segmentation of time series into subseries-level patches which are ...
Yuqi Nie +3 more
semanticscholar +1 more source
Long-term Forecasting with TiDE: Time-series Dense Encoder [PDF]
Trans. Mach. Learn. Res., 2023 Recent work has shown that simple linear models can outperform several Transformer based approaches in long term time-series forecasting. Motivated by this, we propose a Multi-layer Perceptron (MLP) based encoder-decoder model, Time-series Dense Encoder (
Abhimanyu Das +5 more
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