Using time-series forecasting
Some patterns might emerge as variables change over time, whether it’s the price of a stock or the sales figures of a product. Investigating the change of such a variable and using the observed trends to make estimates about the future performance thereof allows you to predict the future. You know that different forecasts exist to predict additive and multiplicative time series.
Reflecting on what you have learned, answer the following questions:
Q.1 where could you use time series forecasting to make predictions in your business environment?
Q.2 what are the implications of the time-related uncertainty involved in these predictions?
Q.1 Time series analysis is a statistical technique to analyze the pattern of data points taken over time to forecast the future. This is applicable to numerous fields of endeavor and business is not an exception.
Time series models can be used to make predictions in the business environment in the following ways;
- It can be used to predict sales
- It can also be used to predict price.
- It can be used to monitor growth in business
- It can be used to forecast customer satisfaction
- It can be used to forecast staff turnover.
- It can be used to predict customer spending habits.
Q.2 There is a degree of uncertainty in all-time predictions. We would not even use the term predict if there were no uncertainty in our statements about the usage of time for future tasks. Some of the implications of the time-related uncertainty involved are:
- It could lead to large financial loss
- It leads to loss of customers if prediction goes wrong
- It could influence overstocking of goods which will eventually lead to loss
- Overconfidence in the accuracy of time predictions often leads to loss.
- An investment established on the basis of time series prediction could be folded up if the prediction goes wrong.