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Forecasting is a critical task in decision-making across numerous domains.
Exponential smoothing: The state of the art
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Strictly proper scoring rules, prediction, and estimation
Gneiting, T. and Raftery, A. E · 2007
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Forecasting with exponential smoothing: the state space approach
Hyndman, R., Koehler, A. B., Ord, J. K., and Snyder, R. D · 2008
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Comparing density forecasts using threshold- and quantile-weighted scoring rules
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Zamo, M. and Naveau, P · 2018
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GluonTS: Probabilistic and Neural Time Series Modeling in Python
Alexandrov, A., Benidis, K., Bohlke-Schneider, M., Flunkert, V., Gasthaus, J., Januschowski, T., Maddix, D. C., Rangapuram, S., Salinas, D., Schulz, J., Stella, L., Türkmen, A. C., and Wang, Y · 2020
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On the opportunities and risks of foundation models
Bommasani, R., Hudson, D. A., Adeli, E., Altman, R., Arora, S., von Arx, S., Bernstein, M. S., Bohg, J., Bosselut, A., Brunskill, E., et al · 2021
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Monash time series forecasting archive
Godahewa, R., Bergmeir, C., Webb, G. I., Hyndman, R. J., and Montero-Manso, P · 2021
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Time-series forecasting with deep learning: a survey
Lim, B. and Zohren, S · 2021
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Fast: Financial news and tweet based time aware network for stock trading
Sawhney, R., Wadhwa, A., Agarwal, S., and Shah, R · 2021
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Finetuned language models are zero-shot learners
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Informer: Beyond efficient transformer for long sequence time-series forecasting
Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., and Zhang, W · 2021
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Evaluating forecasts for high-impact events using transformed kernel scores
Allen, S., Ginsbourger, D., and Ziegel, J · 2023
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Long sequence time-series forecasting with deep learning: A survey
Chen, Z., Ma, M., Li, T., Wang, H., and Li, C · 2023
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Garza, A., Challu, C., and Mergenthaler-Canseco, M · 2023
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Lag-Llama: Towards foundation models for time series forecasting
Rasul, K., Ashok, A., Williams, A. R., Khorasani, A., Adamopoulos, G., Bhagwatkar, R., Biloš, M., Ghonia, H., Hassen, N. V., Schneider, A., et al · 2023
Time-LLM: Time series forecasting by reprogramming large language models
Jin, M., Wang, S., Ma, L., Chu, Z., Zhang, J. Y., Shi, X., Chen, P.-Y., Liang, Y., Li, Y.-F., Pan, S., and Wen, Q · 2024
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Multi-modal forecaster: Jointly predicting time series and textual data
Kim, K., Tsai, H., Sen, R., Das, A., Zhou, Z., Tanpure, A., Luo, M., and Yu, R · 2024
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Foundation models for time series analysis: A tutorial and survey
Liang, Y., Wen, H., Nie, Y., Jiang, Y., Jin, M., Song, D., Pan, S., and Wen, Q · 2024
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Unitime: A language-empowered unified model for cross-domain time series forecasting
Liu, X., Hu, J., Li, Y., Diao, S., Liang, Y., Hooi, B., and Zimmermann, R · 2024
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Language models still struggle to zero-shot reason about time series
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Promptcast: A new prompt-based learning paradigm for time series forecasting
Xue, H. and Salim, F. D · 2023
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Insight miner: A large-scale multimodal model for insight mining from time series
Zhang, Y., Zhang, Y., Zheng, M., Chen, K., Gao, C., Ge, R., Teng, S., Jelloul, A., Rao, J., Guo, X., Fang, C.-W., Zheng, Z., and Yang, J · 2023
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Xforecast: Evaluating natural language explanations for time series forecasting
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Chronos: Learning the language of time series
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Towards time series reasoning with llms
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Syscaps: Language interfaces for simulation surrogates of complex systems
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Merrill, M. A., Tan, M., Gupta, V., Hartvigsen, T., and Althoff, T · 2024
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Implicit reasoning in deep time series forecasting
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LLM processes: Numerical predictive distributions conditioned on natural language
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Unemployment rate [various locations], 2024
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Interventions des pompiers de montréal, 2020
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Unified training of universal time series forecasting transformers
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Beyond trend and periodicity: Guiding time series forecasting with textual cues
Xu, Z., Bian, Y., Zhong, J., Wen, X., and Xu, Q · 2024
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Qwen2 technical report
Yang, A., Yang, B., Hui, B., Zheng, B., Yu, B., Zhou, C., Li, C., Li, C., Liu, D., Huang, F., et al · 2024
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Beyond forecasting: Compositional time series reasoning for end-to-end task execution
Ye, W., Zhang, Y., Yang, W., Tang, L., Cao, D., Cai, J., and Liu, Y · 2024
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Dualtime: A dual-adapter multimodal language model for time series representation
Zhang, W., Ye, J., Li, Z., Li, J., and Tsung, F · 2024
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Position: Empowering time series reasoning with multimodal llms
Kong, Y., Yang, Y., Wang, S., Liu, C., Liang, Y., Jin, M., Zohren, S., Pei, D., Liu, Y., and Wen, Q · 2025
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Chattime: A unified multimodal time series foundation model bridging numerical and textual data
Wang, C., Qi, Q., Wang, J., Sun, H., Zhuang, Z., Wu, J., Zhang, L., and Liao, J · 2025
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Dual-forecaster: A multimodal time series model integrating descriptive and predictive texts, 2025
Wu, W., Zhang, G., tan zheng, Wang, Y., and Qi, H · 2025
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