> For the complete documentation index, see [llms.txt](https://docs.claret.app/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.claret.app/sell/forecast-workbench/forecasting-models.md). # Forecasting Models The statistical models available to use in Claret are mathematical models that use historical data, or other sale types you wish, to predict what will happen in future. The models available are: ### AR - Autoregressive Models Autoregression uses observations from previous time steps (lags) as input to a linear regression equation to predict the value at the next time step. It will first establish the lags that correlate most and then apply them as input variables to a linear regression model to predict the future value. Autoregressive models assume the future to tightly resemble the past and as such will most likely be chosen for clean and seasonal time series. Links: * ### ARIMA - Autoregressive Integrated Moving Average ARIMA models are an evolution of Autoregressive models. They too predict future values based on past values with the help of a linear regression, which takes past values as an input (that's the AR part of ARIMA). But they transform the past values a little more by (1) removing trend and seasonality to focus the prediction on the underlying data and (2) smoothing these values with a moving average. ARIMA models are widely used and popular as they can adapt to many types of time series. Links: * * ### ETS - Exponential Smoothing Forecasts produced by Exponential Smoothing methods are weighted averages of past observations, with the weights decaying exponentially as the observations get older. In other words, the more recent the observation the higher the associated weight. ETS models come in different degrees of complexity from simple methods to models including trend and seasonal components. Links: * ### Naïve methods The Naïve method simply uses the last value of the observations as future predictions. Seasonal Naïve methods (SNAÏVE) identify seasonal periods in the data (months, quarters, or else) to set each future value accordingly. While simple, naïve methods are surprisingly accurate particularly for noisy data. Links: * ### TSLM - Time Series Linear Model A Time Series Linear Model (TSLM) is a linear regression model that predicts the course of one time series based on another time series. An example would be predicting sales based on marketing expenditure. An example for a single time series would be predicting future values based on previous values. Links: * ### VAR - Vector Autoregression The VAR model is a more complex model allowing all variables of the model to affect each other. With simple linear regression forecasts, inputs were affecting outputs unidirectionally. A VAR makes sense when relating multiple time series to each other that can affect each other, for example if we wanted to predict sales based on income, we would assume that higher incomes drive sales, which might positively affect incomes. Links: * --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter: ``` GET https://docs.claret.app/sell/forecast-workbench/forecasting-models.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.