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Implementation of the Data Analytics Methods for the Forecast
To predict season diseases, either statistical or structural models can be used (Hyndman and Athanasopoulos, 2018).
Statistical methods considered in this investigation include the following:
Figure 9.5 Features of the decision tree.
On the other hand, from the existing structural methods the following were selected:
Besides the methods mentioned above, another classification could be applied:
According to the suggested classification in the Table 9.1, there are systemized strong and weak sides of above-mentioned approaches.
For the estimation of the accuracy of prediction methods, time series forecasting error rates will be used (Hyndman and Koehler, 2006).
Table 9.1 Comparison of the Methods and Models
The most common time series forecasting errors are as presented below:
MAPE - mean absolute percentage error:
MAE - mean absolute error:
MSE - mean square error:
RMSE - root mean square error:
ME - mean error:
SD - standard deviation:
Forecast accuracy is an opposite concept to the prediction error. If the forecast error is large, then the accuracy is small and, conversely, if the prediction error is small, then the accuracy is large (Khair et al., 2017). In fact, the forecast error estimate is the inverse of the forecast accuracy - the dependence is simple here:
Forecast accuracy in% = 100% - MAPE (9.8)
Usually, the accuracy is not estimated, in other words, solving the task of forecasting is always evaluated, that is, determine the value of the prediction error, that is, the magnitude and the forecast error. However, it should be understood that if so, then the prediction accuracy = 95%. When talking about high accuracy, we always talk about low forecast error, and there should be no misunderstanding in this area.
In this case, the MAPE is a quantitative estimate of the error itself, and this value clearly tells us the accuracy of prediction, based on the above simple formula. Thus, when estimating the error, we always estimate the accuracy of the prediction.
According to Table 9.2, the best model is a neural network with a nonlinear autoregressive model. You can see the results for the forecast related to Figure 9.6.
Table 9.2 Comparison of Different Methods for the Colds Forecasts
Figure 9.6 Forecast with neural network with nonlinear autoregressive model.
Implementation of the Data Analytics Methods for the Football Matches Forecasts
For the football matches forecasts let us consider following methods (Harville, 2003):
Table 9.3 Comparison of Different Methods for the Football Matches Forecasts
According to Table 9.3, the best model is the neural network of deep learning (the lowest error rate is 6.51).