How do you incorporate external factors and variables into your time series models in Excel?

2 Choose the model type

The next step is to choose the type of time series model that can accommodate the external factors and variables. There are different types of time series models, such as ARIMA, ETS, or regression, that have different assumptions and features. You need to choose the model that best fits your data and your factors. For example, if you have a linear relationship between your data and your factors, you may use a regression model. If you have a nonlinear or complex relationship, you may use an ARIMA or ETS model with exogenous variables.

3 Prepare the data and factors

The third step is to prepare your data and factors for modeling. You need to make sure that your data and factors are in the same frequency, format, and range. You also need to check for missing values, outliers, or errors in your data and factors. You may need to transform, normalize, or standardize your data and factors to make them suitable for modeling. For example, if you are using a regression model, you may need to log-transform your data and factors to reduce skewness and heteroscedasticity.

4 Build the model in Excel

The fourth step is to build the model in Excel using the appropriate functions or tools. You can use the built-in functions such as LINEST, TREND, or FORECAST to create a regression model with external factors and variables. You can also use the Analysis ToolPak add-in to create an ARIMA or ETS model with exogenous variables. You need to specify the parameters and options for your model, such as the order, seasonality, or confidence level. You can also use the Solver add-in to optimize your model parameters.

5 Evaluate the model performance

The fifth step is to evaluate the performance of your model using various metrics and tests. You need to compare the actual and predicted values of your data and factors and calculate the error measures, such as MAE, MSE, or RMSE. You also need to check the residuals of your model and test for autocorrelation, normality, or stationarity. You can use the built-in functions such as CORREL, NORMSINV, or ADFTEST to perform these tests. You can also use charts and graphs to visualize your model results and residuals.

6 Update and refine the model

The final step is to update and refine your model based on new data and factors. You need to monitor your model performance over time and see if it still fits your data and factors well. You may need to add, remove, or modify your factors and variables based on changing conditions or trends. You may also need to re-estimate or re-fit your model parameters based on new data and factors. You can use the built-in functions or tools to update and refine your model in Excel.

7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?