Forecasting Best Practices

Time series forecasting is one of the most important topics in data science. Almost every business needs to predict the future in order to make better decisions and allocate resources more effectively.

This repository provides examples and best practice guidelines for building forecasting solutions. The goal of this repository is to build a comprehensive set of tools and examples that leverage recent advances in forecasting algorithms to build solutions and operationalize them. Rather than creating implementations from scratch, we draw from existing state-of-the-art libraries and build additional utilities around processing and featurizing the data, optimizing and evaluating models, and scaling up to the cloud.

The examples and best practices are provided as Python Jupyter notebooks and R markdown files and a library of utility functions. We hope that these examples and utilities can significantly reduce the “time to market” by simplifying the experience from defining the business…

Deep learning for mlr3. Contribute to mlr-org/mlr3keras development by creating an account on GitHub.

Extra recipes for categorical predictor embeddings - tidymodels/embed

Deep learning for mlr3. Contribute to mlr-org/mlr3keras development by creating an account on GitHub.

Encodes columns of type factor, character and ordered.
PipeOpEncodeLmer() converts factor levels of each factorial column to the
estimated coefficients of a simple random intercept model.
Models are fitted with the glmer function of the lme4 package and are
of the type target ~ 1 + (1 | factor).
If the task is a regression task, the numeric target
variable is used as dependent variable and the factor is used for grouping.
If the task is a classification task, the target variable is used as dependent variable
and the factor is used for grouping.
If the target variable is multiclass, for each level of the multiclass target variable,
binary "one vs. rest" models are fitted.
For training, multiple models can be estimated in a cross-validation scheme
to ensure that the same factor level does not always result in identical
values in the converted numerical feature.
For prediction, a global model (which was fitted on all observations
during training) is used for each factor.
New factor levels are converted to the value…