[forecast][GluonTS] method in the time-series forecast
Background
Support both data researchers and production use cases in the novel design and a fully-managed service, respectively. The further introduction is on the below website from Amazon(R) company. Of course, please obey their policy using this package framework.
Installation
The installation process is handy without too much trouble upon the mentioned website content. However, please consider using the mxnet package in 1.4.1 version from an unexpected issue. The issue log and command are below for your own reference.
issue log: “mxnet.base.MXNetError: vector<T> too long”
issue reference: https://github.com/awslabs/gluon-ts/issues/495
command: pip install — upgrade mxnet==1.4.1 gluonts
Test Example
Let’s check out the example code to make sure the environment ready. Some example files are on the GitHub website for reference. Some files are tested properly.
This article tests the example code from this website with slight modification.
Example code
Example test code from the above website. It is for environment test purposes only. The parameter-setting are the freq, prediction_length, and epochs in the Line21.
The model training time is 46.57 seconds in the i7–5820K CPU machines. During the model training, all CPU cores are working but no signs on GPU’s work in the resource monitor report. The test result is below with the shades of the forecast possibility and median curve.
Conclusion and future work
This framework is strength in the
- a. easy installation with stable solvers
- b. forecast possibility report
- c. support all CPU cores working, and
- d. instinctive tuning parameters.
In the future, the trained model explanation and polynomial equation test case should be done to check its accuracy.