# Analysis of Factors Influencing the Prices of Tourist Offers
## 3 Selecting Optimal Price Predictors
### Discussion  
First of all, it should be mentioned that the two types of features, i.e., time-varying and constant ones, play different roles in solving the stated classification problem. Time-varying parameters are obviously those that may have an impact on the price changes. Constant parameters, on the other hand, may cluster data into various groups, where the prices follow different rules. In our experiments, we analyzed all of them together, as most of the applied methods are tree-based methods, where the feature space is sequentially partitioned on the basis of selected features. In that way, data from various space regions may traverse various decision paths on the basis of all available parameters. Another possible approach could be applying a clustering algorithm only on the basis of constant attributes and then train a separate classifier for each cluster on the basis of time-varying features. Feature rankings obtained for the different values of \(n\) do not vary much. The current price per oil barrel (_barrel_price_) and USD average exchange rate (_usd_avg_) are at the top in each case of \(n\), both for the flight and own transport type. An interesting observation is made for features representing the rate of changes of the above two parameters, i.e., _barrel_price_series_ and _usd_avg_series_. They are also significant predictors, as they usually appear in the top 10 subsets, but it can be observed that the lower \(n\), the higher the position of these two features. It means they are better in shorter time predictions. Another essential predictor is _delta_, representing the number of days a trip is already offered. In each case, it  
Figure 11: Final evaluation of features for \(n=28\). Blue and orange bars present the sum of the weights obtained by all five methods. Black bars indicate the result obtained using sequential backward elimination. takes places from three to five, no matter the transport type and the value of \(n\). Among features that are not time-varying, the _tour_distance_ is the most significant, no matter the transport type. Weather features did not turn out to be important. They usually fall at the end of the ranking list. The only exceptions are current temperatures in the destination and the starting location (_destination_to_current_temp_ and _destination_from_current_temp_) in the case of flight trips for \(n=7\). This means that changes of weather are not essential for more predictions in the longer horizon. Comparisons between trips with flight and own transport indicate strong similarities. The same parameters are on the top of the ranking lists. There are several features that are often higher in the case of their own transport. These are _fitness_, _attraction_, and _Inflation_rate_PL_. On the other hand, _month_ and _changes_ appear to be better predictors in the case of flight transport. Apart from the _fitness_, which has already been mentioned, other hotel attributes do not demonstrate a high significance in solving the classification task. It is also interesting to analyze the results of the sequential backward elimination, which is one of the wrapper methods that lets us find a subset optimal from the point of view of the accuracy of the applied model. The weights assigned to the features using this algorithm have been demonstrated using black bars in Figures 8-11. To indicate an optimal \(m\)-element subset of features, it is important to make sure that the set of the top \(m\) predictors presented on the obtained ranking list contain a complete subset of features identified by the sequential method. There might be a feature, which has the discriminative power in combination with others, but individually it does not appear to be significant. Such a feature would not achieve high results using the methods that treat features independently, but it would using the method that takes into account feature correlations, e.g., sequential backward elimination.