| ... | ... | @@ -168,4 +168,24 @@ _RNN graph representation. Herein, both the first and second RNN layers only con |
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## Additional references
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- [Spurious correlations](https://www.tylervigen.com/spurious-correlations)
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- [Machine Learning Strategies for Time Series Forecasting](https://link.springer.com/chapter/10.1007/978-3-642-36318-4_3)
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- [Understanding LSTM Networks](http://colah.github.io/posts/2015-08-Understanding-LSTMs/)
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- [Illustrated Guide to LSTM’s and GRU’s](https://towardsdatascience.com/illustrated-guide-to-lstms-and-gru-s-a-step-by-step-explanation-44e9eb85bf21)
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-[Exploring lstms](http://blog.echen.me/2017/05/30/exploring-lstms/)
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- [Seq2Seq LSTM Model in Keras](https://towardsdatascience.com/how-to-implement-seq2seq-lstm-model-in-keras-shortcutnlp-6f355f3e5639)
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- [Develop a Seq2Seq Model for Neural Machine Translation](https://machinelearningmastery.com/define-encoder-decoder-sequence-sequence-model-neural-machine-translation-keras/)
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- [tf-seq2seq](https://google.github.io/seq2seq/)
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- [Attention and Augmented Recurrent Neural Networks](https://distill.pub/2016/augmented-rnns/)
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Example case studies
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- [A sequential model-based approach for gas turbine performance diagnostics](https://www.sciencedirect.com/science/article/abs/pii/S036054422032764X?dgcid=raven_sd_aip_email)
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- []()
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- []()
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- [Time Series Split with Scikit-learn](https://medium.com/keita-starts-data-science/time-series-split-with-scikit-learn-74f5be38489e)
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- [Time Series Data in Python](https://www.pluralsight.com/guides/machine-learning-for-time-series-data-in-python)
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- []()
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