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

arXiv: https://arxiv.org/abs/2110.10233 · HTML: https://mlsanity.com/paper/2110.10233/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 14 references that are in the map.
- First posted on arXiv 19 Oct 2021.

## Builds on (most cited first, 12 of 12)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks](https://mlsanity.com/paper/1703.03400/) (2017)
- [On First-Order Meta-Learning Algorithms](https://mlsanity.com/paper/1803.02999/) (2018)
- [DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networks](https://mlsanity.com/paper/1704.04110/) (2017)
- [Temporal Fusion Transformers for Interpretable Multi-horizon Time Series Forecasting](https://mlsanity.com/paper/1912.09363/) (2019)
- [N-BEATS: Neural basis expansion analysis for interpretable time series forecasting](https://mlsanity.com/paper/1905.10437/) (2019)
- [Meta-learning framework with applications to zero-shot time-series forecasting](https://mlsanity.com/paper/2002.02887/) (2020)
- [TimeNet: Pre-trained deep recurrent neural network for time series classification](https://mlsanity.com/paper/1706.08838/) (2017)
- [Do We Really Need Deep Learning Models for Time Series Forecasting?](https://mlsanity.com/paper/2101.02118/) (2021)
- [Dynamical Models of Stock Prices Based on Technical Trading Rules Part II: Analysis of the Models](https://mlsanity.com/paper/1401.1891/) (2014)
- [Dynamical Models of Stock Prices Based on Technical Trading Rules Part I: The Models](https://mlsanity.com/paper/1401.1888/) (2014)
- [Dynamical Models of Stock Prices Based on Technical Trading Rules Part III: Application to Hong Kong Stocks](https://mlsanity.com/paper/1401.1892/) (2014)

## Built on by (most cited first, 3 of 3)

- [Neuro-symbolic Meta Reinforcement Learning for Trading](https://mlsanity.com/paper/2302.08996/) (2023)
- [Learning to Liquidate Forex: Optimal Stopping via Adaptive Top-K Regression](https://mlsanity.com/paper/2202.12578/) (2022)
- [Extreme Volatility Warning under Label Scarcity via Multi-Source Anomaly Fusion](https://mlsanity.com/paper/2607.23682/) (2026)

## Abstract (arXiv)

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