Time Series Forecast with Seasonality and Trend Using Regression in Excel
Learn how to perform time series forecasting in Excel using regression analysis that accounts for both seasonality and trend. This step-by-step tutorial walks you through building a regression model and using it to make future predictions—perfect for sales data and quarterly forecasting.
What You'll Learn:
How to build a regression equation with seasonal dummy variables and a time trend
How to predict future values (e.g. Year 5, Quarter 1) using your regression model
How to evaluate your model’s accuracy by calculating Mean Squared Error (MSE)
Example Forecast:
To forecast Year 5, Q1, you’ll plug values into your regression equation:
y = b₀ + b₁q₁ + b₂t
With:
b₀ = 0.145625 (intercept)
b₁ = -1.36313 (coefficient for Q1)
q₁ = 1 (Q1 dummy variable)
t = 17 (since Year 1, Q1 = t=1 … Year 4, Q4 = t=16)
You’ll also learn to compare predicted vs. actual values across 16 time points and compute the MSE to assess model accuracy—remember, the lower the MSE, the better the model.
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