# Analysis of Factors Influencing the Prices of Tourist Offers
## 2 Related Work
The authors implemented classical linear regression and least squares SVM with an artificial bee colony to optimize its parameters. The latter method let us achieve higher prediction accuracy. The method was implemented in a web application both to alert users when the price of a product changed and to predict the price for the next day. It might be useful for customers, as it enables monitoring product pricing. House prices have always been of particular interest. In [18], the authors analyzed a number of papers on significant factors influencing house prices. Among various determinants grouped into locational, structural, and neighbourhood, the locational ones turned out to be the most significant in fixing the prices of houses. Various methods were applied to create models able to predict prices of houses, e.g., support vector regression, neural networks, and gradient boosting. The hedonic model, which applies multivariate regression analysis, is the simplest way to estimate the significance of various factors. The obtained regression coefficients may be treated as factor weights indicating their influence on the target variable [19]. Interesting findings were presented in [20]. It has been demonstrated that partitioning the data on the basis of selected factors and applying a multi-task learning algorithm may improve the performance of house price prediction. There are also some research studies where dynamic factor approach, which is a tool commonly used for analyzing economic data, is applied in forecasting some tourism indices. Classical dynamic factor models represent the whole cross-section dynamics by a few common factors found, for example, by PCA and then perform forecasting for a target variable on the basis of these common factors using a simple linear regression model. It has been applied, for example, in [21] to predict checking-in and overnight stays of travellers in Spain on the basis of the numbers of queries on several traveling related topics. The authors managed to obtain good results for short-term forecasts. To relax the limitation of the classical dynamic factor model, which does not allow for correlations between idiosyncratic components, a generalised dynamic factor model (GDFM) [22] was proposed. This model was applied by the authors of [23]. They created a time-varying parameter factor vector autoregressive model based on GDFM to analyze the influence of six factors (dining, transportation, attractions, shopping, tours, and lodging) on tourist demand in different periods of time. The model investigated the time-varying characteristics of the relationship between variables. The original six factors were extracted from search queries and represented by selected keyword frequencies. They found that tourism demand responded more intensely to shock in dining, tours, and lodging than to the other three factors. Dining, attractions, and shopping had a positive effect on tourism demand, whereas transportation, tours, and lodging had negative effect. The presented analysis is rather unique, as it is a simultaneous quantitative analysis of several factors. As already mentioned in the Introduction, time varying factors (see Table 1), as well as the price of the trip itself, change over time. Any change to the trip price can be treated as an event in the price change process. This event may be the consequence or cause of other events. This allows for the use of business process models to implement the price change process. The authors of [24] presented the process model in the form of Colored Petri Net (CPN) for determining activities related to changes in the prices of stocks. They analyzed trends of change, rather than changes in individual prices, as the prices of stocks fluctuate on almost every stock quote. Trends influence decisions to buy, sell, or hold stocks, and the presented CPN helps users make that decision. The model is predefined and immutable. Price changes at tour operators occur much less frequently, often without clear trends, so each increase or decrease can be analyzed.