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 and Explanation:

Time-series forecasting:

Naive Approach: is a type of time series forecasting that forecasts next demand to be equal to the current demand

Moving averages: is a type of time series forecasting that uses average of date to forecast the data for next period

Exponential Smoothing: is a type of time series forecasting that uses past date and plugs in exponential formula.

Trend projection: is a type of time series forecasting that uses past data and predicts the trend for the future

Associative forecasting:

Linear regression: relationship between the independent and dependent variables.

For time series forecasting many companies can use it. The retail stores, FMCG companies (fast moving consumer goods), fertilizer company, food and beverages etc. Time series helps you forecast sales and demand. Which is helpful for any company.

For associative forecasting, companies like real estate would be interested in using liner regression. Where the real estate would want to know the price of a house in a neighborhood based on the factors such as size of house, number of bedroom, proximity to school etc.

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