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mainmagic/
chronos-t5-small-btc-m1

Name: chronos-t5-small-btc-m1
Availability: InStock

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chronos-t5-small-btc-m1

This is a Chronos model fine-tuned on financial time series data. The model is based on the T5 architecture and is designed for time series forecasting.

Model Description

Model Type: Chronos (T5-based time series forecasting model)
Fine-tuned from: amazon/chronos-t5-small
Uploaded by: mainmagic
Date: 2025-04-06

Chronos model fine-tuned on BTC/USD M1 data for time series forecasting

Performance Metrics

Metric	Value
mse	1.0823
mae	0.8172
mape	16552.9256

Usage

python

# Import the Chronos pipeline
# Note: You may need to adjust the import path based on your installation
import sys
sys.path.append('/path/to/chronos-forecasting/src')  # Adjust this path
from chronos.chronos import ChronosPipeline
import torch

# Load the model
pipeline = ChronosPipeline.from_pretrained("mainmagic/chronos-t5-small-btc-m1")

# Create input data (example)
context = torch.randn(1, 512)  # Batch size 1, context length 512

# Generate forecast
forecast = pipeline.predict(
    context,
    prediction_length=60,  # Predict 60 steps ahead
    num_samples=20  # Generate 20 different forecast trajectories
)

# Use median as point forecast
median_forecast = torch.median(forecast, dim=1)[0]

Training Details

This model was fine-tuned using the Chronos native training scripts. The model was trained on financial time series data with the following parameters:

Context length: 512
Prediction length: 60
Optimizer: adamw_torch
Learning rate: 0.0001
Batch size: 16
Gradient accumulation steps: 4

Limitations

This model is specifically trained for financial time series forecasting and may not perform well on other types of time series data. The model's performance may also vary depending on market conditions and the specific financial instrument being forecasted.

Citation

If you use this model, please cite:

bibtex

@misc{chronos-forecasting,
  author = {Amazon Science},
  title = {Chronos: Learning the Language of Time Series},
  year = {2024},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/amazon-science/chronos-forecasting}}
}