Which statement accurately describes time-series forecasting?

Time-series forecasting is a method that predicts future demand by analyzing historical data points collected over time. This approach focuses on identifying patterns, trends, and seasonal variations in past demand data to inform and project future outcomes. By leveraging past demand data, businesses can make informed predictions about what to expect in the future, which is crucial for inventory management, production planning, and overall supply chain efficiency.

The first statement accurately captures the core principle of time-series forecasting, emphasizing the relationship between past demand patterns and future projections. This method does not imply that future demand will exactly mirror past demand, nor does it solely rely on seasonal patterns or assert that it is always superior to associative forecasting. Instead, it uses a comprehensive analysis of historical demand data to form predictions that account for various influences over time.