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Deep-learning techniques have been successfully used for time-seriesforecasting and have often shown superior performance on many standardbenchmark datasets as compared to traditional techniques. Here we present acomprehensive and comparative study of performance of deep-learning techniquesfor forecasting prices in financial markets. We benchmark state-of-the-artdeep-learning baselines, such as NBeats, etc., on data from currency as well asstock markets. We also generate synthetic data using a fuzzy-logic based modelof demand driven by technical rules such as moving averages, which are oftenused by traders. We benchmark the baseline techniques on this synthetic data aswell as use it for data augmentation. We also apply gradient-basedmeta-learning to account for non-stationarity of financial time-series. Ourextensive experiments notwithstanding, the surprising result is that thestandard ARIMA models outperforms deep-learning even using data augmentation ormeta-learning. We conclude by speculating as to why this might be the case.

Forecasting Market Prices using DL with Data Augmentation and Meta-learning: ARIMA still wins!