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   https://doi.org/10.1109/TCNS.2020.3029992  

   Garcia, Alfredo; Wang, Luochao; Huang, Jeff; Hong, Lingzhou (March 2021, IEEE Transactions on Control of Network Systems)

   null (Ed.)
2. Discrete-time Chen Series for Time Discretization and Machine Learning

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   Gray, W. Steven; Venkatesh, G. S.; Espinosa, Luis A. (March 2019, 2019 53rd Annual Conference on Information Sciences and Systems (CISS))
3. Incremental Learning in Time-series Data using Reinforcement Learning

   https://doi.org/10.1109/ICDMW58026.2022.00115  

   Shuqair, Mustafa; Jimenez-Shahed, Joohi; Ghoraani, Behnaz (November 2022, IEEE)
4. Towards Real-Time Temporal Graph Learning

   https://doi.org/10.1109/ICCD56317.2022.00047  

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5. Causal Learning with Interrupted Time Series

   Zhang, Yiwen; Rottman, Benjamin (January 2021, Proceedings of the Annual Conference of the Cognitive Science Society)

   Fitch, T; Lamm, C; Leder, H; Tessmar, K (Ed.)

   Interrupted time series analysis (ITSA) is a statistical procedure that evaluates whether an intervention causes a change in the intercept and/or slope of the time series. However, very little research has accessed causal learning in interrupted time series situations. We systematically investigated whether people are able to learn causal influences from a process akin to ITSA, and compared four different presentation formats of stimuli. We found that participants’ judgments agreed with ITSA in cases in which the pre-intervention slope is zero or in the same direction as the changes in intercept or slope. How- ever, participants had considerable difficulty controlling for pre-intervention slope when it is in the opposite direction of the changes in intercept or slope. The presentation formats didn’t affect judgments in most cases, but did in one. We discuss these results in terms of two potential heuristics that people might use aside from a process akin to ITSA. 

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