Models Used in finnts

arima

univariate, local

modeltime, forecast

Regression model that is based on finding relationships between lagged values of the target variable you are trying to forecast.

arima-boost

multivariate, local

modeltime, forecast, xgboost

Arima model (refer to arima) that models the trend compoent of target variable, then uses xgboost model (refer to xgboost) to train on the remaining residuals.

cubist

multivariate, local, global, ensemble

rules

Hybrid of tree based and linear regression approach. Many decision trees are built, but regression coefficients are used at each terminal node instead of averging values in other tree based approaches.

croston

univariate, local

modeltime, forecast

Useful for intermittent demand forecasting, aka when there are a lot of periods of zero values. Involves simple exponential smoothing on non-zero values of target variable and another application of seasonal exponential smoothing on periods between non-zero elements of the target variable. Refer to ets for more details on exponential smoothing.

deepar

univariate, local, global

modeltime.gluonts, reticulate, gluonts and torch (Python)

Autoregressive neural network (deep learning).

ets

univariate, local

modeltime, forecast

Forecasts produced using exponential smoothing methods are weighted averages of past observations, with the weights decaying exponentially as the observations get older. Exponential smoothing models try to forecast the components of a time series which can be broken down in to error, trend, and seasonality. These components can be forecasted separately then either added or multiplied together to get the final forecast output.

glmnet

multivariate, local, global, ensemble

parsnip, glmnet

Linear regression (line of best fit) with regularization to help prevent overfitting and built in variable selection.

mars

multivariate, local, global

parsnip, earth

An extension to linear regression that captures nonlinearities and interactions between variables.

meanf

univariate, local

modeltime, forecast

Simple average of previous year of target variable values.

nbeats

univariate, local, global

modeltime.gluonts, reticulate, gluonts (Python)

Another neural network (deep learning).

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Univariate vs Multivariate Models

Univariate models only use the date and target variable values when producing a forecast. They are mostly common on various statistical forecasting models like arima and ets.
Multivariate models leverage many features when producing a forecast, provided as input data before model training. These features can be automatically created using internal feature engineering techniques within the package, or provided as external regressors. Most common machine learning models today, like xgboost and svm-poly, are multivariate models. An important thing to note is that multivariate models provided in the package can leverage different recipes of feature engineering, that contain different techniques of creating features. These can be identified by seeing the letter “R” followed by a number like “1” or “2” added onto the end of multivariate models. For example, “glmnet-R1” uses a specific recipe to engineer features before feeding the data into a glmnet model to train.

Global vs Local Models

Global models take the entire dataset across all individual time series and model them all at once within a single model. Global models are only ran if the input data contains more than one individual time series.
Local models take each individual time series from the input data and model them separately.

Ensemble Models

Ensemble models are trained on predictions made by individual models. For example, a “glmnet-ensemble” model takes forecasts made by each individual model and feeds them as training data into a glmnet model. They also contain features related to date and forecasting horizon.

Deep Learning Models

The package contains multiple deep learning models that initially come from a python package called gluonts, which is developed by Amazon. They are called into R through a package called reticulate, which let’s python code run within R. In order to make these models run within R, you will need to install the proper python libraries, check out modeltime.gluonts to learn more about how to install everything properly.

Leveraging the Tidymodels Framework

Most of the models within the package are built on a fantastic time series library called modeltime, which was built on top tidymodels. Tidymodels is a fantastic series of packages that help in feature engineering (recipes), hyperparameter tuning (tune), model training (parsnip), and back testing (resample). Big shout out to the modeltime and tidymodels teams for being the shoulders this package stands on!