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Recently, diffusion probabilistic models have attracted attention in generative time series forecasting due to their remarkable capacity to generate high-fidelity samples.
Long Short-Term Memory
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Attention is all you need
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Normalizing kalman filters for multivariate time series analysis
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Stock trend prediction with multi-granularity data: A contrastive learning approach with adaptive fusion
Min Hou, Chang Xu, Yang Liu, Weiqing Liu, Jiang Bian, Le Wu, Zhi Li, Enhong Chen, and Tie-Yan Liu · 2021
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Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting
Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, and Wancai Zhang · 2021
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Tactis: Transformer-attentional copulas for time series
Alexandre Drouin, Étienne Marcotte, and Nicolas Chapados · 2022
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Classifier-free diffusion guidance
Jonathan Ho and Tim Salimans · 2022
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Generative time series forecasting with diffusion, denoise, and disentanglement
Yan Li, Xinjiang Lu, Yaqing Wang, and Dejing Dou · 2022
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A time series is worth 64 words: Long-term forecasting with transformers
Yuqi Nie, Nam H Nguyen, Phanwadee Sinthong, and Jayant Kalagnanam · 2022
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Gluonts: Probabilistic and neural time series modeling in python
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Pranav Rajpurkar, Emma Chen, Oishi Banerjee, and Eric J Topol · 2022
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Generating high fidelity data from low-density regions using diffusion models
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Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting
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Non-autoregressive conditional diffusion models for time series prediction
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