Improved support for conflicting package dependencies on Windows Operating Systems. Solution is to separate the installation process into two stages, which happens inside of
pytorch-lightning 1.3.8 depends on numpy>=1.17.2 mxnet 1.7.0.post1 depends on numpy<1.17.0 and >=1.8.2
Users can now
Modeltime GluonTS now support
gluonts 0.8.0. Simply run
install_gluonts() to upgrade. The upgraded support makes
modeltime.gluonts incompatible with earlier versions of GluonTS (e.g.
gluonts 0.6.3). The solution is to upgrade to
gluonts 0.8.0, which requires:
Additionally, GluonTS 0.8.0 now supports pytorch as a backend. Use
install_gluonts(include_pytorch = TRUE) to simplify installation of the PyTorch backend. Pytorch backend requirements:
A new engine has been added to
deep_ar() that enables the Pytorch backend using
set_engine("torch"). This requires the Python packages
install_gluonts(include_pytorch = TRUE) to simplify installation.
A new function,
gp_forecaster(), integrates the Gaussian Process Estimator from GluonTS.
A new function,
deep_state(), integrates the Deep State Estimator from GluonTS.
We’ve updated the Installation Guide. This includes revised requirements for installation, upgrading to
modeltime.gluonts >= 0.3.0, troubleshooting installation, python environment requirements, and custom python environments.
We’ve updated the Getting Started Guide to go through a DeepAR example.
We’ve update the GPU Setup Instructions to cover Modeltime >=0.3.0.
install_gluonts(): Gains two new parameters to help with upgrading:
fresh_install: If TRUE, will remove prior installations of the r-glounts conda environment to setup for a fresh installation. This can be useful if errors appear during upgrades. Default: FALSE.
include_pytorch: If TRUE, will install torch. Needed for Torch implementation of deep_ar(). Default: FALSE.
Improvements made to connect with the GluonTS Python Environment on Startup.
Internal Scaling by Group: After significant testing it appears that some data sets return better results when the data is scaled by time series “id” (group). To help facilitate this, a new option is available scale by id:
scale = TRUE.
Custom Python Environments: Provide an option for setting a Custom Python Environment by supplying a
GLUONTS_PYTHON environment variable. Before running
Sys.setenv(GLUONTS_PYTHON = 'path/to/python') to set the path of your python executable in a Conda or Virtual Environment that has ‘gluonts’, ‘mxnet’, ‘numpy’, ‘pandas’ and ‘pathlib’ available as dependencies.
gluonts==0.6.3. This upgrade improves forecast accuracy.
.onLoadmessage to provide options for configuring the Python Environment.