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Multivariate time series forecasting (MTSF) aims to learn temporal dynamics among variables to forecast future time series.
Traffic flow forecasting: comparison of modeling approaches
Smith, B. L.; and Demetsky, M. J. 1997 · 1997
Earlier work this paper cites.
UCI machine learning repository
Asuncion, A.; and Newman, D. 2007 · 2007
Earlier work this paper cites.
FRED-MD: A monthly database for macroeconomic research
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Earlier work this paper cites.
Improving language understanding by generative pre-training
Radford, A.; Narasimhan, K.; Salimans, T.; Sutskever, I.; et al. 2018 · 2018
Earlier work this paper cites.
Language models are unsupervised multitask learners
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Earlier work this paper cites.
Towards Semantic Travel Behavior Prediction for Private Car Users
Chen, H.; Wang, D.; and Liu, C. 2020 · 2020
Earlier work this paper cites.
Developing a deep learning framework with two-stage feature selection for multivariate financial time series forecasting
Niu, T.; Wang, J.; Lu, H.; Yang, W.; and Du, P. 2020 · 2020
Earlier work this paper cites.
On Layer Normalization in the Transformer Architecture
Xiong, R.; Yang, Y.; He, D.; Zheng, K.; Zheng, S.; Xing, C.; Zhang, H.; Lan, Y.; Wang, L.; and Liu, T. 2020 · 2020
Earlier work this paper cites.
Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecasting
Wu, H.; Xu, J.; Wang, J.; and Long, M. 2021 · 2021
Earlier work this paper cites.
Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting
Zhang, S.; Peng, J.; Zhang, S.; Li, J.; Xiong, H.; and Zhang, W. 2021 · 2021
Earlier work this paper cites.
Adaptive Dual-View WaveNet for urban spatial–temporal event prediction
Jin, G.; Liu, C.; Xi, Z.; Sha, H.; Liu, Y.; and Huang, J. 2022 · 2022
Earlier work this paper cites.
Reversible Instance Normalization for Accurate Time-Series Forecasting against Distribution Shift
Kim, T.; Kim, J.; Tae, Y.; Park, C.; Choi, J.; and Choo, J. 2022 · 2022
Earlier work this paper cites.
Foreseeing private car transfer between urban regions with multiple graph-based generative adversarial networks
Liu, C.; Xiao, Z.; Wang, D.; Cheng, M.; Chen, H.; and Cai, J. 2022 · 2022
Earlier work this paper cites.
MBA-STNet: Bayes-enhanced Discriminative Multi-task Learning for Flow Prediction
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Vehicle Trajectory Interpolation Based on Ensemble Transfer Regression
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Leveraging language foundation models for human mobility forecasting
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FEDformer: Frequency Enhanced Decomposed Transformer for Long-term Series Forecasting
Zhou, T.; Ma, Z.; Wen, Q.; Wang, X.; Sun, L.; and Jin, R. 2022 · 2022
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Large Language Models Are Zero-Shot Time Series Forecasters
Gruver, N.; Finzi, M.; Qiu, S.; and Wilson, A. G. 2023 · 2023
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Normalization Techniques in Training DNNs: Methodology, Analysis and Application
Modeling dynamic spatiotemporal user preference for location prediction: a mutually enhanced method
Cai, J.; Wang, D.; Chen, H.; Liu, C.; and Xiao, Z. 2024 · 2024
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TEMPO: Prompt-based Generative Pre-trained Transformer for Time Series Forecasting
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TimeDRL: Disentangled Representation Learning for Multivariate Time-Series
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LeRet: Language-Empowered Retentive Network for Time Series Forecasting
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GPT4MTS: Prompt-based Large Language Model for Multimodal Time-series Forecasting
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Foundation models for time series analysis: A tutorial and survey
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iTransformer: Inverted Transformers Are Effective for Time Series Forecasting
Liu, Y.; Hu, T.; Zhang, H.; Wu, H.; Wang, S.; Ma, L.; and Long, M. 2023 · 2023
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A Time Series is Worth 64 Words: Long-term Forecasting with Transformers
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PromptCast: A New Prompt-Based Learning Paradigm for Time Series Forecasting
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Are Transformers Effective for Time Series Forecasting?
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One Fits All: Power General Time Series Analysis by Pretrained LM
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Not All Tokens Are What You Need for Pretraining
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A unified replay-based continuous learning framework for spatio-temporal prediction on streaming data
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S 2 {}^{\mbox{2}} IP-LLM: Semantic Space Informed Prompt Learning with LLM for Time Series Forecasting
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TFB: Towards Comprehensive and Fair Benchmarking of Time Series Forecasting Methods
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TEST: Text Prototype Aligned Embedding to Activate LLM’s Ability for Time Series
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