Financial Forecasting With α-RNNs: A Time Series Modeling Approach - Dixon, Matthew; London, Justin (Frontiers in Applied Mathematics and Statistics, 2021)
The era of modern financial data modeling seeks machine learning techniques which are suitable for noisy and non-stationary big data. We demonstrate how a general class of exponential smoothed recurrent neural networks ($α$-RNNs) are well suited to modeling dynamical systems arising in big data applications such as high frequency and algorithmic trading. Application of exponentially smoothed RNNs to minute level Bitcoin prices and CME futures tick data, highlight the efficacy of exponential smoothing for multi-step time series forecasting. Our $α$-RNNs are also compared with more complex, `black-box', architectures such as GRUs and LSTMs and shown to provide comparable performance, but with far fewer model parameters and network complexity.