Faster feature selection for global model training
Added seasonal_period within prep_models()
for more control over multiple seasonal periods in models like
tbats
Bug Fixes
Error in formatting of training data for global models
Error when using multiple external regressors with future
values
Remove target_log_transformation within
prep_data(), since box_cox has now replaced it
for automated power transformations
Error when running hierarchical forecasts with weekly data
finnts 0.5.0
Improvements
Added support for hierarchical forecasting with external
regressors
Allow global models for hierarchical forecasts
Multistep horizon forecasts for R1 recipe, listed as
multistep_horizon within prep_data()
Always save the most accurate model average, regardless if selected
as best model. This allows for improved scaling with large data
sets.
Automatically condense large forecasts (+3k time series) into
smaller amount of files to make it easier to read forecast outputs
Improved weighted MAPE calculation across all time series
Changed default for box_cox argument in prep_data() to
FALSE
Support for spark version 3.4 in Azure Synapse/Fabric
Bug Fixes
Error in run_type column join in final forecast output
Error in running feature selection
Breaking Changes
Minimum R version now set to R 4.0 to comply with package dependency
minimum version for tune
finnts 0.4.0
Improvements
Tidymodels speed up
Added external regressor support for ARIMA by introducing a new
model option of arimax, which uses engineered features in
addition to any external regressors supplied.
Automated feature selection, refer to feature selection vignette for
more details
Error handling in hierarchical forecast reconciliation
Box-cox and differencing transformations
Added new function, list_models(), that lists available
models in the package
Bug Fixes
Best model selection
Hierarchical forecast reconciliation
finnts 0.3.0
Improvements
Spark data frame support. Initial input data can now be a spark data
frame, enabling millions of time series to be ran across a spark compute
cluster.
Updated train/validation/test process for multivariate ML
models.
In addition to existing forecast_time_series(), added
new sub components of the finnts forecast process that can be called
separately or in a production pipeline. Allows for more control of the
forecast process
prep_data()
prep_models()
train_models()
ensemble_models()
final_models()
Automated read and write capabilities. Intermediate and final Finn
outputs are now automatically written to disk (see options below). This
creates better MLOps capabilities, easier scale on spark, and better
fault tolerance by not needing to start the whole forecast process over
from scratch if an error occurred.
Temporary location on local machine, which will then get deleted
after R session is closed.
Path on local machine or a mounted Azure Data Lake Storage path in
spark to save the intermediate and final Finn run results.
Azure Blob Storage to store non-spark runs on a data lake.
SharePoint/OneDrive storage to store non-spark runs within M365.
New MLOps features that allow you to retrieve the final trained
models through get_trained_models(), get specific run
information thorough get_run_info(), and even retrieve the
initial feature engineered data through
get_prepped_data().
Deprecated
run_model_parallel has been replaced with
inner_parallel within
forecast_time_series()
Data being returned as a list when running
forecast_time_series(). Instead please use
get_forecast_data() to retrieve Finn forecast outputs.
Breaking Changes
No longer support for Azure Batch parallel processing, please use
spark instead
Parallel processing through spark now needs a mounted Azure Data
Lake Storage path supplied through set_run_info(). Please
refer to the vignettes for more details.
finnts 0.2.4
Dependency Fixes
Fixed dependency issue with timetk.
finnts 0.2.3
Dependency Fixes
Removed package dependency modeltime.gluonts and its deep learning
models because the package is no longer on CRAN.
finnts 0.2.2
Bug Fixes
Fixed hierarchical forecast reconciliation issues for certain
forecasts that have high residuals.
Compliant with latest dplyr v1.1.0
finnts 0.2.1
Bug Fixes
Fixed feature engineering issue around NaN/Inf values when computing
log values of negative external regressor values.
Fixed issue of ensuring random seed is set correctly in parallel
processing.
finnts 0.2.0
Improvements
Added spark support to run Finn in parallel on Azure Databricks or
Azure Synapse.
Added error handling when creating simple model averages. Should
allow forecast to keep running even if there are memory issues when
averaging individual forecast models, which helps on large data
sets.
Expand Azure Batch task timeout from one day to one week. Prevents
errors when running large forecasts that take over a day to run in Azure
Batch.
Deprecated
Deprecated azure_batch parallel compute option within
forecast_time_series function since the Azure Batch R packages are
deprecated. Please use the new integration with spark on Azure.
finnts 0.1.1
Default Function Behavior
Change default behavior to only run R1 feature engineering recipe
when the argument run_global_models is set to TRUE or NULL and
recipes_to_run is set to NULL in the forecast_time_series function.
Running R2 recipe with global models on large data sets often results in
RAM issues when running in Azure Batch.
Bug Fixes
Fixed error when converting infinite values to NA values after model
forecasts are created.
Changed the cubist model to reference the new cubist model
definition in parsnip package.
Fixed bug in hierarchical forecasting. Missing values in the
hierarchy are converted from NA to zero, which fixes how data is
aggregated at various levels of hierarchy.