|
--- |
|
library_name: transformers |
|
base_model: |
|
- amazon/chronos-bolt-small |
|
pipeline_tag: time-series-forecasting |
|
tags: |
|
- forecasting |
|
- time series |
|
- intermitent demand |
|
--- |
|
# Model Card for Chronos Bolt Small Fine-Tuned Model v3 |
|
|
|
|
|
<img align="center" height="350" src="https://media3.giphy.com/media/v1.Y2lkPTc5MGI3NjExYjRmcWUwaGFkbW1lczJoYzBjbHBxZjMyeDdhdDQycGdzamwyOGhiZyZlcD12MV9pbnRlcm5hbF9naWZfYnlfaWQmY3Q9Zw/ZtB2l3jHiJsFa/giphy.gif"/> </p> |
|
|
|
|
|
|
|
The model was fine-tuned on a proprietary dataset containing 25 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. |
|
- Also added additional exogenous information about the target series (5 additional columns; 2 of them are different types of volumes of the target series). |
|
|
|
WQL = 0.5908 |
|
|
|
This large-scale dataset ensures the model captures complex patterns and temporal dependencies necessary for accurate forecasting. |
|
|
|
|
|
#### Summary |
|
|
|
The fine-tuned model performs well on intermitent demand forecasting. |
|
|
|
## Technical Specifications |
|
|
|
### Model Architecture and Objective |
|
|
|
The model is based on the `amazon/chronos-bolt-small` architecture, fine-tuned specifically for intermittent time-series forecasting tasks. It leverages pre-trained capabilities for sequence-to-sequence modeling, adapted to handle multi-horizon forecasting scenarios. |
|
|
|
## Contact: |
|
|
|
[NIEXCHE](https://niexche.github.io/) |
|
|
|
[NIEXCHE (Fevzi KILAS)](https://fevzikilas.tech/NOTES/) |
