References
- Shi, X., Chen, Z., Wang, H., Yeung, D. Y., Wong, W. K., & Woo, W. C. (2015). Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting. In Advances in Neural Information Processing Systems (pp. 802–810) https://papers.nips.cc/paper/2015/hash/07563a3fe3bbe7e3ba84431ad9d055af-Abstract.html
- Karim, F., Majumdar, S., Darabi, H., & Harford, S. (2019). LSTM fully convolutional networks for time series classification. IEEE Access, 7, 1662-1669
- Siami-Namini, S., Tavakoli, N., & Siami Namin, A. (2019). The Performance of LSTM and BiLSTM in Forecasting Time Series. In 2019 IEEE International Conference on Big Data
- TensorFlow learning rate scheduler: https://www.tensorflow.org/api_docs/python/tf/keras/callbacks/LearningRateScheduler
- Lambda layers: https://keras.io/api/layers/core_layers/lambda/
- Time series forecasting: https://www.tensorflow.org/tutorials/structured_data/time_series
- tf.data: Build TensorFlow...
