Univariate, time series models

A simple model using the last value as a prediction for the future.

Prophet is a procedure for forecasting time series data based on an additive model where non-linear trends are fit with yearly, weekly, and daily seasonality, plus holiday effects. It works best with time series that have strong seasonal effects and several seasons of historical data. Prophet is robust to missing data and shifts in the trend, and typically handles outliers well.

An artificial neural network (ANN) is a model which is based on a collection of connected units or nodes called artificial neurons, which loosely model the neurons in a biological brain.

TBATS is a time series model that is useful for handling data with multiple seasonal patterns. TBATS is an acronym for key features of the model: T: Trigonometric seasonality B: Box-Cox transformation A: ARIMA errors T: Trend S: Seasonal components.

STL is a versatile and robust method for decomposing time series. STL is an acronym for “Seasonal and Trend decomposition using Loess”, while Loess is a method for estimating nonlinear relationships.

The Theta model is a simple method for forecasting that involves fitting two theta-lines, forecasting the lines using simple exponential smoothing, and then combining the forecasts from the two lines to produce the final forecast.

Exponential smoothing is a rule of thumb technique for smoothing time series data using the exponential window function.

An autoregressive integrated moving average (ARIMA) model is a generalization of an autoregressive moving average (ARMA) model.