Time series forecasting could be a critical demand in real-life senarios, such as network loading prediction, server utilization prediction, traffic flow prediction and so on. While the developing process for an AI end-to-end could be error-prone and time-consuming, let alone meeting a strict accuracy or performance requirement.
BigDL Time Series Toolkit supports building end-to-end (data loading, processing, built-in model, training, tuning, inference) AI solution on single node or cluster.
- More than 10 built-in models for forecasting and anomaly detection
- Performance tuning for extreme latency/throughput on Intel hardware
- Data processing, model training and hyperparameter tunning on single node or cluster
BigDL Time Series Toolkit and the workflow example shown below could be run widely on both Core™ and Xeon® series processers.
| Recommended Hardware | Precision | |
|---|---|---|
| CPU | Intel® 4th Gen Xeon® Scalable Performance processors | BF16 |
| CPU | Intel® 1st, 2nd, 3rd, and 4th Gen Xeon® Scalable Performance processors | FP32/INT8 |
Provided example workflow (https://github.com/intel/BigDL-Time-Series-Toolkit/edit/main/DEVCATALOG.md) shows how to use history taxi traffic number in past 1 day and predict future 30 min's taxi traffic number.
- Document First page (you may start from here and navigate to other pages)
- How to Guides (If you are meeting with some specific problems during the usage, how-to guides are good place to be checked.)
- Example & Use-case library (Examples provides short, high quality use case that users can emulated in their own works.)
Nothing for now
The BigDL Time Series Toolkit team tracks both bugs and enhancement requests using GitHub issues.