Which forecasting technique uses evidence from the past, seasonal patterns, and trends to predict future events?

Time series analysis is a forecasting technique that systematically uses historical data to identify patterns over time, including trends and seasonal variations. This method operates on the premise that past events can provide insight into future occurrences, making it highly effective for predicting future values of a variable based on its historical behavior.

In time series analysis, data points are collected over specific intervals, allowing for the detection of trends (long-term movements in data) and seasonal patterns (recurring fluctuations that occur at regular intervals, such as monthly or quarterly). By analyzing this historical data, forecasts can be developed that account for these identified patterns, improving the accuracy of predictions.

Other forecasting methods, such as qualitative forecasting, rely on expert opinions and subjective judgment rather than historical data. Market research analysis typically focuses on gathering data about market needs and consumer preferences, which is not specifically a predictive technique based on historical patterns. A systematic approach might refer to a structured methodology in problem-solving or analysis but does not inherently imply the use of historical data to forecast future events.

Therefore, time series analysis stands out as the most appropriate technique in this context, as it directly utilizes past evidence to inform forecasts, making it the ideal choice for predicting future occurrences based on identified trends and patterns.