Which forecasting approach uses historical data to identify patterns and predict future values?

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Multiple Choice

Which forecasting approach uses historical data to identify patterns and predict future values?

Explanation:
The key idea is forecasting based on patterns in data collected over time. Time-series forecasting looks at historical observations collected in sequence to identify patterns such as trends, seasonality, and cycles, and then uses that learned structure to predict future values. For example, a business might track monthly sales for several years and notice a steady upward trend plus higher sales in holidays; a time-series approach would use those patterns to forecast next month’s sales. The Delphi method relies on consensus from experts rather than data patterns. Regression analysis examines how a dependent variable changes with one or more predictors, which can include time but isn’t inherently about uncovering time-based patterns. Market research gathers information about markets and consumer preferences, not specifically about modeling sequential data for forecasting.

The key idea is forecasting based on patterns in data collected over time. Time-series forecasting looks at historical observations collected in sequence to identify patterns such as trends, seasonality, and cycles, and then uses that learned structure to predict future values. For example, a business might track monthly sales for several years and notice a steady upward trend plus higher sales in holidays; a time-series approach would use those patterns to forecast next month’s sales. The Delphi method relies on consensus from experts rather than data patterns. Regression analysis examines how a dependent variable changes with one or more predictors, which can include time but isn’t inherently about uncovering time-based patterns. Market research gathers information about markets and consumer preferences, not specifically about modeling sequential data for forecasting.

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