---
library_name: transformers
base_model:
- amazon/chronos-t5-small
pipeline_tag: time-series-forecasting
---
# Model Card for Chronos T5 Small Fine-Tuned Model


<img align="center" height="350" src="https://media3.giphy.com/media/v1.Y2lkPTc5MGI3NjExYjRmcWUwaGFkbW1lczJoYzBjbHBxZjMyeDdhdDQycGdzamwyOGhiZyZlcD12MV9pbnRlcm5hbF9naWZfYnlfaWQmY3Q9Zw/ZtB2l3jHiJsFa/giphy.gif"/> </p>


## Summary

This model is fine-tuned for time-series forecasting tasks and serves as a tool for both practical predictions and research into time-series modeling. It is based on the `amazon/chronos-t5-small` architecture and has been adapted using a dataset with 15 million rows of proprietary time-series data. Due to confidentiality restrictions, dataset details cannot be shared.

## Fine-Tuning Dataset

The model was fine-tuned on a proprietary dataset containing 15 million rows of time-series data. While details about the dataset are confidential, the following general characteristics are provided:
- The dataset consists of multi-dimensional time-series data.  
- Features include historical values, contextual attributes, and external covariates relevant to forecasting.  
- The data spans multiple domains, enabling generalization across a wide range of forecasting tasks.  

This large-scale dataset ensures the model captures complex patterns and temporal dependencies necessary for accurate forecasting.

## Evaluation

### Testing Data, Factors & Metrics

#### Testing Data

The model was evaluated using several publicly available time-series datasets, including:
- **electricity_15min**  
- **monash_electricity_hourly**  
- **monash_electricity_weekly**  
- **monash_kdd_cup_2018**  
- **monash_pedestrian_counts**  

#### Factors

Evaluation was conducted across datasets representing various domains such as electricity usage, pedestrian counts, and competition data.

#### Metrics

Two primary metrics were used for evaluation:
- **MASE (Mean Absolute Scaled Error):** A normalized metric for assessing forecast accuracy.
- **WQL (Weighted Quantile Loss):** Measures the quality of probabilistic predictions.

### Results

| Dataset                     | Model                        | MASE   | WQL     |
|-----------------------------|------------------------------|--------|---------|
| electricity_15min           | amazon/chronos-t5-small     | 0.425  | 0.085   |
| monash_electricity_hourly   | amazon/chronos-t5-small     | 1.537  | 0.110   |
| monash_electricity_weekly   | amazon/chronos-t5-small     | 1.943  | 0.086   |
| monash_kdd_cup_2018         | amazon/chronos-t5-small     | 0.693  | 0.309   |
| monash_pedestrian_counts    | amazon/chronos-t5-small     | 0.308  | 0.247   |

#### Summary

The fine-tuned model performs well on short-term electricity datasets (e.g., **electricity_15min**) with low MASE and WQL values. Performance varies depending on the dataset's characteristics, particularly with longer-term or aggregated data.

## Technical Specifications

### Model Architecture and Objective

The model is based on the `amazon/chronos-t5-small` architecture, fine-tuned specifically for time-series forecasting tasks. It leverages pre-trained capabilities for sequence-to-sequence modeling, adapted to handle multi-horizon forecasting scenarios.

## Citation

If you use this model in your research or applications, please cite it as:

```bibtex
@misc{Fevzi2024LLaMA-2-7B-NIEXCHE,
  author = {Fevzi KILAS},
  title = {LLaMA-2-7B-NIEXCHE: A Turkish Agriculture QA Model},
  year = {2024},
  howpublished = {https://huggingface.co/NIEXCHE/turkish_agriculture_QA_llama2_22.6k}
}
```
## Contact:

[NIEXCHE (Fevzi KILAS)](https://niexche.github.io/) 

![header](https://capsule-render.vercel.app/api?type=venom&height=150&text=👋%20NIEXCHE&textBg=false&fontColor=f3c1c0&fontAlign=46&animation=blink&stroke=800000&strokeWidth=45section=header)