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:
