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Time-series forecasting (TSF) finds broad applications in real-world scenarios.
Analytical and numerical studies of multiplicative noise
Sancho, J. M., San Miguel, M., Katz, S., and Gunton, J · 1982
Earlier work this paper cites.
Learning representations by back-propagating errors
Rumelhart, D. E., Hinton, G. E., and Williams, R. J · 1986
Earlier work this paper cites.
Long short-term memory
Hochreiter, S. and Schmidhuber, J · 1997
Earlier work this paper cites.
Multiplicative panel data models without the strict exogeneity assumption
Wooldridge, J. M · 1997
Earlier work this paper cites.
Causality: Models, reasoning and inference
Pearl, J. et al · 2000
Earlier work this paper cites.
Time series and forecasting: Brief history and future research
Tsay, R. S · 2000
Earlier work this paper cites.
Time series forecasting using a hybrid arima and neural network model
Zhang, G. P · 2003
Earlier work this paper cites.
Exposure to cold and respiratory tract infections
Mourtzoukou, E. and Falagas, M. E · 2007
Earlier work this paper cites.
Nonlinear causal discovery with additive noise models
Hoyer, P., Janzing, D., Mooij, J. M., Peters, J., and Schölkopf, B · 2008
Earlier work this paper cites.
Cold temperature and low humidity are associated with increased occurrence of respiratory tract infections
Mäkinen, T. M., Juvonen, R., Jokelainen, J., Harju, T. H., Peitso, A., Bloigu, A., Silvennoinen-Kassinen, S., Leinonen, M., and Hassi, J · 2009
Earlier work this paper cites.
The statistical analysis of time series
Anderson, T. W · 2011
Earlier work this paper cites.
Stock price prediction using the arima model
Ariyo, A. A., Adewumi, A. O., and Ayo, C. K · 2014
Earlier work this paper cites.
Analysis of economic time series: a synthesis
Nerlove, M., Grether, D. M., and Carvalho, J. L · 2014
Earlier work this paper cites.
Time series forecasting for nonlinear and non-stationary processes: a review and comparative study
Cheng, C., Sa-Ngasoongsong, A., Beyca, O., Le, T., Yang, H., Kong, Z., and Bukkapatnam, S. T · 2015
Earlier work this paper cites.
Domain adaptation with conditional transferable components
Gong, M., Zhang, K., Liu, T., Tao, D., Glymour, C., and Schölkopf, B · 2016
Earlier work this paper cites.
Causal inference by using invariant prediction: identification and confidence intervals
Peters, J., Bühlmann, P., and Meinshausen, N · 2016
Earlier work this paper cites.
Attention is all you need
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., and Polosukhin, I · 2017
Earlier work this paper cites.
Modeling long-and short-term temporal patterns with deep neural networks
Lai, G., Chang, W.-C., Yang, Y., and Liu, H · 2018
Earlier work this paper cites.
Invariant models for causal transfer learning
Rojas-Carulla, M., Schölkopf, B., Turner, R., and Peters, J · 2018
Earlier work this paper cites.
Arjovsky, M., Bottou, L., Gulrajani, I., and Lopez-Paz, D · 2019
Cited alongside, same era.
Deep adaptive input normalization for time series forecasting
Passalis, N., Tefas, A., Kanniainen, J., Gabbouj, M., and Iosifidis, A · 2019
Cited alongside, same era.
Sagawa, S., Koh, P. W., Hashimoto, T. B., and Liang, P · 2019
Cited alongside, same era.
Invariant rationalization
Chang, S., Zhang, Y., Yu, M., and Jaakkola, T · 2020
Cited alongside, same era.
Reformer: The efficient transformer
Kitaev, N., Kaiser, Ł., and Levskaya, A · 2020
Cited alongside, same era.
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
Later among the works it cites.
Woods: Benchmarks for out-of-distribution generalization in time series
Gagnon-Audet, J.-C., Ahuja, K., Darvishi-Bayazi, M.-J., Mousavi, P., Dumas, G., and Rish, I · 2022
Later among the works it cites.
Environment diversification with multi-head neural network for invariant learning
Huang, B.-W., Liao, K.-T., Kao, C.-S., and Lin, S.-D · 2022
Later among the works it cites.
Camul: Calibrated and accurate multi-view time-series forecasting
Kamarthi, H., Kong, L., Rodríguez, A., Zhang, C., and Prakash, B. A · 2022
Later among the works it cites.
Zin: When and how to learn invariance without environment partition?
Lin, Y., Zhu, S., Tan, L., and Cui, P · 2022
Later among the works it cites.
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Koyama, M. and Yamaguchi, S · 2020
Cited alongside, same era.
Financial time series forecasting with deep learning: A systematic literature review: 2005–2019
Sezer, O. B., Gudelek, M. U., and Ozbayoglu, A. M · 2020
Cited alongside, same era.
Invariance principle meets information bottleneck for out-of-distribution generalization
Ahuja, K., Caballero, E., Zhang, D., Gagnon-Audet, J.-C., Bengio, Y., Mitliagkas, I., and Rish, I · 2021
Cited alongside, same era.
Environment inference for invariant learning
Creager, E., Jacobsen, J.-H., and Zemel, R · 2021
Cited alongside, same era.
Adarnn: Adaptive learning and forecasting of time series
Du, Y., Wang, J., Feng, W., Pan, S., Qin, T., Xu, R., and Wang, C · 2021
Cited alongside, same era.
Reversible instance normalization for accurate time-series forecasting against distribution shift
Kim, T., Kim, J., Tae, Y., Park, C., Choi, J.-H., and Choo, J · 2021
Cited alongside, same era.
Out-of-distribution generalization via risk extrapolation (rex)
Krueger, D., Caballero, E., Jacobsen, J.-H., Zhang, A., Binas, J., Zhang, D., Le Priol, R., and Courville, A · 2021
Cited alongside, same era.
Non-stationary transformers: Exploring the stationarity in time series forecasting
Liu, Y., Wu, H., Wang, J., and Long, M · 2022
Later among the works it cites.
Out-of-distribution representation learning for time series classification
Lu, W., Wang, J., Sun, X., Chen, Y., and Xie, X · 2022
Later among the works it cites.
A time series is worth 64 words: Long-term forecasting with transformers
Nie, Y., Nguyen, N. H., Sinthong, P., and Kalagnanam, J · 2022
Later among the works it cites.
Certifying out-of-domain generalization for blackbox functions
Weber, M. G., Li, L., Wang, B., Zhao, Z., Li, B., and Zhang, C · 2022
Later among the works it cites.
Timesnet: Temporal 2d-variation modeling for general time series analysis
Wu, H., Hu, T., Liu, Y., Zhou, H., Wang, J., and Long, M · 2022
Later among the works it cites.
Crossformer: Transformer utilizing cross-dimension dependency for multivariate time series forecasting
Zhang, Y. and Yan, J · 2022
Later among the works it cites.
Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting
Zhou, T., Ma, Z., Wen, Q., Wang, X., Sun, L., and Jin, R · 2022
Later among the works it cites.
Dish-ts: a general paradigm for alleviating distribution shift in time series forecasting
Fan, W., Wang, P., Wang, D., Wang, D., Zhou, Y., and Fu, Y · 2023
Later among the works it cites.
Large language models are zero-shot time series forecasters
Gruver, N., Finzi, M., Qiu, S., and Wilson, A. G · 2023
Later among the works it cites.
Diversify: A general framework for time series out-of-distribution detection and generalization
Lu, W., Wang, J., Sun, X., Chen, Y., Ji, X., Yang, Q., and Xie, X · 2023
Later among the works it cites.
Certifiable out-of-distribution generalization
Ye, N., Zhu, L., Wang, J., Zeng, Z., Shao, J., Peng, C., Pan, B., Li, K., and Zhu, J · 2023
Later among the works it cites.
Onenet: Enhancing time series forecasting models under concept drift by online ensembling
Zhang, Y.-F., Wen, Q., Wang, X., Chen, W., Sun, L., Zhang, Z., Wang, L., Jin, R., and Tan, T · 2023
Later among the works it cites.
Performative time-series forecasting
Zhao, Z., Rodriguez, A., and Prakash, B. A · 2023
Later among the works it cites.