Rolling forecasts are a method used in time series analysis to generate updated projections for future events based on the latest available data. Instead of creating a static forecast that remains unchanged over a set period, rolling forecasts continuously adjust as new data comes in. This means that at regular intervals—like monthly or quarterly—the forecast is recalculated to reflect the most recent trends and information, ensuring that predictions are as accurate as possible.
For example, a retail company might use rolling forecasts to predict sales for the next six months. Initially, they create a forecast based on the last years' sales data. As each month passes, they take the most recent month’s actual sales figures and update their forecast for the following six months. This way, if there was a spike in sales due to a new marketing campaign or a dip due to external factors like economic changes, the forecast would capture these effects right away, making it more relevant and responsive to the current market conditions.
Rolling forecasts are particularly beneficial for industries where conditions change frequently, such as retail, finance, or technology. By regularly updating predictions, businesses can make informed decisions about inventory, staffing, or budgeting that are based on up-to-date insights rather than outdated assumptions. This approach can help organizations stay agile and competitive in a dynamic environment.