Model Card for Chronos T5 Small Fine-Tuned Model
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:
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