To predict future sales, what method looks closely at how sales have changed in the past?

The method that looks closely at how sales have changed in the past to predict future sales is Time Series Forecasting. This is a statistical technique used to analyze past observations of a variable, in this case, sales, that have been collected sequentially over time. The core idea is that historical patterns, trends, and relationships within the sales data will continue into the future. Time Series Forecasting works by decomposing past sales data into several identifiable components: Trend refers to the overall long-term direction of sales, indicating whether they are generally increasing, decreasing, or remaining stable over an extended period. For example, a growing business might observe an upward trend in its annual sales figures over a decade. Seasonality describes regular, predictable short-term fluctuations in sales that repeat over fixed periods, such as daily, weekly, monthly, or quarterly. An example is a swimsuit company consistently experiencing its highest sales during the summer months each year. Cyclical Patterns are longer-term, non-seasonal fluctuations in sales that typically repeat over periods longer than a year, often influenced by economic cycles or industry-specific conditions. For instance, sales of luxury goods might rise during periods of economic prosperity and fall during recessions over several years. Random Variation, also known as noise, represents the unpredictable, irregular fluctuations in sales that cannot be explained by trends, seasonality, or cyclical patterns. An unexpected, one-off local event that briefly impacts sales is an example of random variation. By identifying and understanding these components from historical sales data, Time Series Forecasting methods project these established patterns forward in time to generate predictions for future sales, operating on the assumption that past behaviors and influences will largely persist.