Forecasting with Recurrent Neural Networks: 12 Tricks

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Deep learning in neural networks: An overview (2014, background): “Compare also more recent RNN algorithms able to deal with long time lags (Schäfer et al., 2006; Martens and Sutskever, 2011; Zimmermann et al., 2012; Koutnı́k et al., 2014).”
Forecasting Across Time Series Databases using Long Short-Term Memory Networks on Groups of Similar Series (2017, background): “…Natural Language Processing (Mikolov et al., 2010), machine translation (Sutskever et al., 2014), and speech recognition (Graves et al., 2013), and are also gaining popularity in time series research (Fei and Yeung, 2015; Paw lowski and Kurach, 2015; Lipton et al., 2015; Zimmermann et al., 2012).”
On Learning to Think: Algorithmic Information Theory for Novel Combinations of Reinforcement Learning Controllers and Recurrent Neural World Models (2015, positive): “Those early CM systems, however, did not yet use powerful RNNs such as LSTM.”
Forecasting across time series databases using recurrent neural networks on groups of similar series: A clustering approach (2017, methodology): “and are also gaining popularity in time series research (Fei and Yeung, 2015; Paw lowski and Kurach, 2015; Lipton et al., 2015; Zimmermann et al., 2012).”
A Practical Guide to Applying Echo State Networks (2012, background): “Teacher forcing applied to speedup error backpropagation RNN training is also discussed in chapter [50] of this book.”