Question

Describe the various types of time-series and associative forecasting models. Which types of organizations are each of these most applicable to and why?

Answer 1

The types of time-series models are: Naïve approach, moving averages, exponential smoothing, and trend projection. An associativeforecasting model is linear regression. The naïve approach is the simplest technique that assumes that the next period will equal to the demand of the most recent period. Moving averages uses an average of data to forecast the upcoming period. Exponential smoothing takes little past data and is weighted by exponential formula. Trend projection uses past data history to set data point to create a trend. Using this trend you are able to project the future slope of the line. Lastly, linear regression is used to compare the relationship between a dependent and an independent variable to find a linear line through data points. I believe any type of business could use time-series forecasting. It is used just to predict the future by past data, which any business from retail to even the stock market could partake in. Associative forecasting is something that real estate and mortgage lenders could us. I currently work in the industry and we use interest rates to predict the housing market and home refinancing.