Statistical analysis of forecasting covid

Sales and demand forecasters have a variety of techniques at their disposal to predict the future. The forecasts using the x-11 technique were based on statistical methods alone, and did not consider any special information. It is usually difficult to make projections from raw data since the rates and trends are not immediately obvious; they are mixed up with seasonal variations, for example, and perhaps distorted by such factors as the effects of a large sales promotion campaign.

As the chart shows, causal models are by far the best for predicting turning points and preparing long-range forecasts. It may be impossible for the company to obtain good information about what is taking place at points further along the flow system (as in the upper segment of exhibit ii), and, in consequence, the forecaster will necessarily be using a different genre of forecasting from what is used for a consumer product.

This suggested to us that a better job of forecasting could be done by combining special knowledge, the techniques of the division, Economics the x-11 method. When a product has entered rapid growth, on the other hand, there are generally sufficient data available to construct statistical and possibly even causal growth models (although the latter will necessarily contain assumptions that must be verified later).

The forecaster might easily overreact to random changes, mistaking them for evidence of a prevailing trend, mistake a change in the growth rate for a seasonal, and so on. We should note that when we developed these forecasts and techniques, we recognized that additional techniques would be necessary at later times to maintain the accuracy that would be needed in subsequent periods.

This technique is a considerable improvement over the moving average technique, which does not adapt quickly to changes in trends and which requires significantly more data storage. This assumption is more likely to be correct over the short term than it is over the long term, and for this reason these techniques provide us with reasonably accurate forecasts for the immediate future but do quite poorly further into the future (unless the data patterns are extraordinarily stable).

A common objection to much long-range forecasting is that it is virtually impossible to predict with accuracy what will happen several years into the future. We have found that an analysis of the patterns of change in the growth rate gives us more accuracy in predicting turning points (and therefore changes from positive to negative growth, and vice versa) than when we use only the trend cycle.