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Deep time series models: A comprehensive survey and benchmark
Time series, characterized by a sequence of data points arranged in a discrete-time order,
are ubiquitous in real-world applications. Different from other modalities, time series present …
are ubiquitous in real-world applications. Different from other modalities, time series present …
A survey on deep learning based time series analysis with frequency transformation
Recently, frequency transformation (FT) has been increasingly incorporated into deep
learning models to significantly enhance state-of-the-art accuracy and efficiency in time …
learning models to significantly enhance state-of-the-art accuracy and efficiency in time …
Artificial intelligence for science in quantum, atomistic, and continuum systems
Advances in artificial intelligence (AI) are fueling a new paradigm of discoveries in natural
sciences. Today, AI has started to advance natural sciences by improving, accelerating, and …
sciences. Today, AI has started to advance natural sciences by improving, accelerating, and …
Simba: Simplified mamba-based architecture for vision and multivariate time series
BN Patro, VS Agneeswaran - arxiv preprint arxiv:2403.15360, 2024 - arxiv.org
Transformers have widely adopted attention networks for sequence mixing and MLPs for
channel mixing, playing a pivotal role in achieving breakthroughs across domains. However …
channel mixing, playing a pivotal role in achieving breakthroughs across domains. However …
Timexer: Empowering transformers for time series forecasting with exogenous variables
Deep models have demonstrated remarkable performance in time series forecasting.
However, due to the partially-observed nature of real-world applications, solely focusing on …
However, due to the partially-observed nature of real-world applications, solely focusing on …
Ginar: An end-to-end multivariate time series forecasting model suitable for variable missing
Multivariate time series forecasting (MTSF) is crucial for decision-making to precisely
forecast the future values/trends, based on the complex relationships identified from …
forecast the future values/trends, based on the complex relationships identified from …
Revisiting multimodal emotion recognition in conversation from the perspective of graph spectrum
Efficiently capturing consistent and complementary semantic features in a multimodal
conversation context is crucial for Multimodal Emotion Recognition in Conversation (MERC) …
conversation context is crucial for Multimodal Emotion Recognition in Conversation (MERC) …
A fuzzy C-means clustering-based hybrid multivariate time series prediction framework with feature selection
J Zhan, X Huang, Y Qian, W Ding - IEEE Transactions on Fuzzy …, 2024 - ieeexplore.ieee.org
Multivariate time series prediction (MTSP) stands as a significant and challenging frontier in
the data science domain, garnering considerable interest among researchers. Extreme …
the data science domain, garnering considerable interest among researchers. Extreme …
Filternet: Harnessing frequency filters for time series forecasting
Given the ubiquitous presence of time series data across various domains, precise
forecasting of time series holds significant importance and finds widespread real-world …
forecasting of time series holds significant importance and finds widespread real-world …
Irregular multivariate time series forecasting: A transformable patching graph neural networks approach
Forecasting of Irregular Multivariate Time Series (IMTS) is critical for numerous areas, such
as healthcare, biomechanics, climate science, and astronomy. Despite existing research …
as healthcare, biomechanics, climate science, and astronomy. Despite existing research …