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Dematos, G., Boyd, M.S., Kermanshahi, B. et al. Feedforward versus recurrent neural networks for forecasting monthly japanese yen exchange rates. Financial Engineering and the Japanese Markets 3, 59–75 (1996). https://doi.org/10.1007/BF00868008
"Feedforward neural networks pass the data forward from input to output, while recurrent networks have a feedback loop where data can be fed back into the input at some point before it is fed forward again for further processing and final output. Some have argued that since time series data may have autocorrelation or time dependence, the recurrent neural network models which take advantage of time dependence may be useful. Feedforward and recurrent neural networks are used for comparison in forecasting the Japanese yen/US dollar exchange rate. A traditional ARIMA model is used as a benchmark for comparison with the neural network models."
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