Zero-shot Application of Amazon Chronos Large Language Model in Financial Time Series Prediction

This article introduces a zero-shot cotton futures price prediction system based on the Amazon Chronos large language model, demonstrating how to transfer the capabilities of pre-trained language models to time series prediction tasks.

Time series prediction has always been a core requirement in industries such as finance, energy, and retail. Traditional methods like ARIMA, Prophet, or deep learning models (LSTM, Transformer) usually require training on specific datasets. However, the Chronos model launched by Amazon in 2024 has changed this landscape—it is a pre-trained time series prediction model based on a language model architecture, capable of directly performing zero-shot predictions on unseen data.

The Vardhaman zero-shot price prediction project builds a complete enterprise-level prediction pipeline, focusing on price prediction for ICE Cotton No.2 Futures (CT1). The system integrates six real-time data sources, constructs a daily multivariate feature store, and uses the Amazon Chronos model to generate prediction results.

1. Data Ingestion Layer: Collects real-time market data from six independent data sources
2. Feature Engineering Layer: Builds a daily updated multivariate feature store
3. Prediction Engine: Zero-shot inference based on Amazon Chronos
4. Result Output: Generates actionable futures price predictions

Chronos represents a significant breakthrough in the field of time series prediction. It tokenizes time series data and then models it using a causal Transformer architecture similar to language models. Key innovations include:

• Unified Representation: Converts numerical time series into discrete token sequences
• Pre-training Advantages: Pre-trained on a large amount of diverse time series data to learn general time series patterns
• Zero-shot Capability: Can produce reasonable predictions without fine-tuning for specific tasks

This project demonstrates how to integrate Chronos into a production-level system:

Cotton futures prices are influenced by multiple factors, including:

• Global supply and demand data
• Weather and climate indicators
• Macroeconomic signals
• Prices of related commodities (crude oil, other agricultural products)
• Market sentiment indicators

The project uses a daily updated multivariate feature store to ensure the consistency and timeliness of model inputs. This design supports seamless switching between historical data backtracking and real-time prediction.