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I need replies to these two responses. Add a question to the end each should be about 150-200 words

1.

Time-series forecasting utilizes data from past years to make a prediction on the future. There are three different models of time series forecasting which are the naive approach, moving averages, and exponential smoothing. Within those models there are four components- trend, seasonality, cycles, and random variables.  The types of organizations that are applicable to this kind of forecasting would be anything that includes sales that are evenly spaced, like in months, quarters, or weeks. So, for example like a monthly sales of Converse shoes or weekly shipment of apples from a farmer to the grocery store. 

Associative forecasting models include trend progression and linear regression. This type includes the variables and factors that might have a role in future sales, which makes this forecast more reliable and realistic because it plans for other costs. "For example, the sales of Dell PCs may be related to Dell’s advertising budget, the company’s prices, competitors’ prices and promotional strategies, and even the nation’s economy and unemployment rates" (Heizer & Render, page 126, 2014). The most commonly used model is linear regression analysis. Organizations use this when wanting to predict a relationship between other departments (payroll, marketing, etc.) and sales. Manufacturing companies and fast food restaurants are two examples of companies that would use this kind of forecasting. 

2.

Time-series is best described as a forecasting technique that shows how past data points can influence behavior in the future. Forecasting puts number sets together that have been collected in the past and creates a pattern that we can work off of to try and base future circumstances off of the past numbers. Its more a hypothesis than an educated guess. Thats not to say that the future might be different from the same time a fiscal year ago. The forecast gets more accurate as the time you're forecasting for get closer too. So if you're trying to forecast the need for beach towels it's going to be easier to forecast that data as you get closer and closer to summer.