# Curriculum Learning in Deep Neural Networks for Financial Forecasting

arXiv: https://arxiv.org/abs/1904.12887 · HTML: https://mlsanity.com/paper/1904.12887/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Apr 2019.

## Built on by (most cited first, 8 of 8)

- [Digital Twin-based Anomaly Detection with Curriculum Learning in Cyber-physical Systems](https://mlsanity.com/paper/2309.15995/) (2023)
- [Machine Learning for Financial Forecasting, Planning and Analysis: Recent Developments and Pitfalls](https://mlsanity.com/paper/2107.04851/) (2021)
- [Improving Deep Metric Learning by Divide and Conquer](https://mlsanity.com/paper/2109.04003/) (2021)
- [Curriculum Learning and Imitation Learning for Model-free Control on Financial Time-series](https://mlsanity.com/paper/2311.13326/) (2023)
- [Regret-Optimized Portfolio Enhancement through Deep Reinforcement Learning and Future Looking Rewards](https://mlsanity.com/paper/2502.02619/) (2025)
- [An Efficient deep learning model to Predict Stock Price Movement Based on Limit Order Book](https://mlsanity.com/paper/2505.22678/) (2025)
- [Optimizing the Training Diet: Data Mixture Search for Robust Time Series Forecasting](https://mlsanity.com/paper/2512.11546/) (2025)
- [Dual-Criterion Curriculum Learning: Application to Temporal Data](https://mlsanity.com/paper/2603.23573/) (2026)

## Abstract (arXiv)

For any financial organization, computing accurate quarterly forecasts for various products is one of the most critical operations. As the granularity at which forecasts are needed increases, traditional statistical time series models may not scale well. We apply deep neural networks in the forecasting domain by experimenting with techniques from Natural Language Processing (Encoder-Decoder LSTMs) and Computer Vision (Dilated CNNs), as well as incorporating transfer learning. A novel contribution of this paper is the application of curriculum learning to neural network models built for time series forecasting. We illustrate the performance of our models using Microsoft's revenue data corresponding to Enterprise, and Small, Medium & Corporate products, spanning approximately 60 regions across the globe for 8 different business segments, and totaling in the order of tens of billions of USD. We compare our models' performance to the ensemble model of traditional statistics and machine learning techniques currently used by Microsoft Finance. With this in-production model as a baseline, our experiments yield an approximately 30% improvement in overall accuracy on test data. We find that our curriculum learning LSTM-based model performs best, showing that it is reasonable to implement our proposed methods without overfitting on medium-sized data.