In the context of financial forecasting, which approach would provide insights over consecutive time intervals?

Time series forecasting is a statistical technique specifically designed to analyze time-ordered data points, enabling predictions about future values based on previously observed values. This approach is highly effective in financial forecasting as it inherently takes into account the temporal dimension of the data, capturing trends, seasonal patterns, and cyclical behaviors over consecutive time intervals.

By focusing on the sequence of data points collected over regular time intervals—such as daily, monthly, or yearly—time series forecasting allows analysts to discern patterns that may emerge over time. This makes it particularly valuable for financial data, where trends can significantly influence decision-making. It helps organizations anticipate future revenues, expenses, and other financial metrics, thereby facilitating better financial planning and strategy.

In contrast, data cleansing focuses on preparing and correcting data for analysis, rather than providing insights on time intervals. Anomaly detection aims to identify unusual data points that deviate from expected patterns, which does not contribute directly to making forecasts over time. Regression analysis, while useful for understanding relationships between variables, does not inherently take the time component into account unless specifically adapted to a time series context. Thus, time series forecasting is the most suitable approach for gaining insights over consecutive time intervals in financial forecasting.