The simple moving method is a quantitative forecasting method that relies on an adjustable set period. The set period remains the same, such as reviewing a six-month period. Every month, you replace the oldest month with the newest month, updating your evaluation period. For instance, if you are evaluating a six-month period from January to June, you would remove January's numbers in July and add July's information in its place. This technique gives equal weight to every month in your period of review.
You can use a weighted moving method to evaluate an evaluation period, similar to the simple moving method, but with a different weight given to each month in the period. The weighted moving method is most effective at evaluating trends with expected monthly changes, such as the sale of seasonal clothing. As an example, if you know that more people travel during the summer months, you can apply a standard multiplier to summer month estimates, increasing the accuracy of your estimates during those months.
The exponential smoothing method allows you to evaluate data, considering recent results as more significant than older periods. This method is ideal for evaluating quantitative data in fields and markets with rapidly changing values, such as sales figures in a volatile market. For instance, if you are evaluating the sales of your newest video game, you can use this method to evaluate earlier sales as less significant and recent sales as more significant, predicting whether sales will remain strong or decrease in the future.
Similar to the exponential smoothing method, you can use the trend-adjusted method to forecast an expected result by comparing the recent market volatility as well as changes in trends. This technique is important when you are evaluating the expected changes to a market from your marketing campaign or changes to the overall market desire for your product. As an example, if you want to evaluate your newest video game sales, while considering the market changes from the expensive marketing work you performed, you can use this technique to arrive at a reliable forecast.