Academic References

Agresti, Alan. 2012. Categorical Data Analysis. Vol. 792. John Wiley & Sons.

Assimakopoulos, Vassilis, and Konstantinos Nikolopoulos. 2000. “The Theta Model: A Decomposition Approach to Forecasting.” International Journal of Forecasting 16 (4): 521–30.

Clark, Kevin, Urvashi Khandelwal, Omer Levy, and Christopher D Manning. 2019. “What Does Bert Look at? An Analysis of Bert’s Attention.” arXiv Preprint arXiv:1906.04341.

Cleveland, Robert B, William S Cleveland, Jean E McRae, Irma Terpenning, et al. 1990. “STL: A Seasonal-Trend Decomposition.” J. Off. Stat 6 (1): 3–73.

Devlin, Jacob, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. “Bert: Pre-Training of Deep Bidirectional Transformers for Language Understanding.” Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), 4171–86.

Dragulescu, Adrian A, and Victor M Yakovenko. 2002. “Statistical Mechanics of Money, Income, and Wealth: A Short Survey.” arXiv Preprint Cond-Mat/0211175.

Draper, NR. 1998. Applied Regression Analysis. McGraw-Hill. Inc.

Group, Stanford NLP. 2014. GloVe: Global Vectors for Word Representation. https://nlp.stanford.edu/projects/glove/.

Han, Xiaochuang, Byron C Wallace, and Yulia Tsvetkov. 2020. “Explaining Black Box Predictions and Unveiling Data Artifacts Through Influence Functions.” arXiv Preprint arXiv:2005.06676.

Hastie, Trevor, Robert Tibshirani, and Jerome Friedman. 2009. An Introduction to Statistical Learning.

Hyndman, Rob J, and George Athanasopoulos. 2018. Forecasting: Principles and Practice. OTexts.

Jurafsky, Daniel, and James H. Martin. 2025. Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition, with Language Models. 3rd ed. https://web.stanford.edu/~jurafsky/slp3/.

Leszczynski, Megan. 2021. Self-Supervised Learning. CS229 Lecture Notes. https://cs229.stanford.edu/notes2021spring/notes2021spring/cs229_lecture_selfsupervision_final.pdf.

Mikolov, Tomas, Kai Chen, Greg Corrado, and Jeffrey Dean. 2013. “Efficient Estimation of Word Representations in Vector Space.” arXiv Preprint arXiv:1301.3781.

Mikolov, Tomas, Ilya Sutskever, Kai Chen, Greg S Corrado, and Jeff Dean. 2013. “Distributed Representations of Words and Phrases and Their Compositionality.” Advances in Neural Information Processing Systems 26.

Molnar, Christoph. 2020. Interpretable Machine Learning. Lulu. com.

Pennington, Jeffrey, Richard Socher, and Christopher D Manning. 2014. “Glove: Global Vectors for Word Representation.” Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), 1532–43.

Rosner, Bernard. 2015. Fundamentals of Biostatistics. Cengage learning.

Sun, Xiaofei, Diyi Yang, Xiaoya Li, et al. 2021. “Interpreting Deep Learning Models in Natural Language Processing: A Review.” arXiv Preprint arXiv:2110.10470.

Szeliski, Richard. 2022. Computer Vision: Algorithms and Applications. Springer Nature.