Many of the statistical time Series are considering Trend, Cyclic and Sessional factors of their own values. However, the real-world data is far from true. External environment factors are impacting the time series most of the time. For example, oil prices do not depend on the historical oil prices themselves. It depends on the world political situation especially the political situation in the middle-east.
Smoking: How large of a global problem is it? And how can we make progress against it? - Our World in Data shows such a case as shown in the following figure.
As you can see from the above figure, USA cigarettes sales are very much dependant on external factors. For example, there is a rise in cigarette sales after the end of world war II ends in 1945. However, imposing the federal tax, banning inflight smoking had resulted in a significant drop in sales of cigarettes.
Further, the lung cancer time series has the same shape as the cigarette sales. This means, by controlling cigarette sales, you can control lung cancer. This is called cross prediction and this can be controlled by ARTxP algorithm/ Furhter, in Azure AutoML public datasets are used to model the above factors.
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