# Conditional Mutual information-based Contrastive Loss for Financial Time Series Forecasting

arXiv: https://arxiv.org/abs/2002.07638 · HTML: https://mlsanity.com/paper/2002.07638/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Feb 2020.

## Built on by (most cited first, 5 of 5)

- [Universal Trading for Order Execution with Oracle Policy Distillation](https://mlsanity.com/paper/2103.10860/) (2021)
- [Protecting the Future: Neonatal Seizure Detection with Spatial-Temporal Modeling](https://mlsanity.com/paper/2307.05382/) (2023)
- [Towards Unified Multimodal Financial Forecasting: Integrating Sentiment Embeddings and Market Indicators via Cross-Modal Attention](https://mlsanity.com/paper/2508.13327/) (2025)
- [Liquidity takers behavior representation through a contrastive learning approach](https://mlsanity.com/paper/2306.05987/) (2023)
- [Information Subtraction: Learning Representations for Conditional Entropy](https://mlsanity.com/paper/2501.02012/) (2025)

## Abstract (arXiv)

We present a representation learning framework for financial time series forecasting. One challenge of using deep learning models for finance forecasting is the shortage of available training data when using small datasets. Direct trend classification using deep neural networks trained on small datasets is susceptible to the overfitting problem. In this paper, we propose to first learn compact representations from time series data, then use the learned representations to train a simpler model for predicting time series movements. We consider a class-conditioned latent variable model. We train an encoder network to maximize the mutual information between the latent variables and the trend information conditioned on the encoded observed variables. We show that conditional mutual information maximization can be approximated by a contrastive loss. Then, the problem is transformed into a classification task of determining whether two encoded representations are sampled from the same class or not. This is equivalent to performing pairwise comparisons of the training datapoints, and thus, improves the generalization ability of the encoder network. We use deep autoregressive models as our encoder to capture long-term dependencies of the sequence data. Empirical experiments indicate that our proposed method has the potential to advance state-of-the-art performance.