Question

A) Suppose you had a large number of variables which you believe might have some forecast information. How would you use these variables to forecast in an efficient way?

B) How would you extend this procedure to allow for dynamic effects?

Answer 1

A)

Forecasting is the process of making predictions of the future based on past and present data and most commonly by analysis of trends. A commonplace example might be estimation of some variable of interest at some specified future date. Prediction is a similar, but more general term. Both might refer to formal statistical methods employing time series, cross-sectional or longitudinal data, or alternatively to less formal judgmental methods. Usage can differ between areas of application: for example, in hydrology the terms "forecast" and "forecasting" are sometimes reserved for estimates of values at certain specific future times, while the term "prediction" is used for more general estimates, such as the number of times floods will occur over a long period

The role of the forecaster in the real world is quite different from that of the mythical seer. Prediction is concerned with future certainty; forecasting looks at how hidden currents in the present signal possible changes in direction for companies, societies, or the world at large. Thus, the primary goal of forecasting is to identify the full range of possibilities, not a limited set of illusory certainties. Whether a specific forecast actually turns out to be accurate is only part of the picture—even a broken clock is right twice a day. Above all, the forecaster’s task is to map uncertainty, for in a world where our actions in the present influence the future, uncertainty is opportunity.

There is a wide range of quantitative forecasting methods, often developed within specific disciplines for specific purposes. Each method has its own properties, accuracies, and costs that must be considered when choosing a specific method. Most quantitative forecasting problems use either time series data (collected at regular intervals over time) or cross-sectional data (collected at a single point in time).

Cross-sectional forecasting

With cross-sectional data, we are wanting to predict the value of something we have not observed, using the information on the cases that we have observed.

Time series forecasting

Time series data are useful when you are forecasting something that is changing over time (e.g., stock prices, sales figures, profits, etc.).

Predictor variables and time series forecasting

Predictor variables can also be used in time series forecasting. For example, suppose we wish to forecast the hourly electricity demand (ED) of a hot region during the summer period.

B)

dynamic analysis can be used to find:
• natural frequency
• dynamic displacements
• time history results
• modal analysis