## 1 Introduction
It can thus be concluded that the correct choice of fuzzy functions is a specific problem, and many researchers and experts have justifiably given the attention of this issue. The next part provides an overview of the literature dealing with finding a suitable fuzzy function. When using different MFs for a given problem, it has usually been found that Gaussian and triangular MFs have very good results, better than other types of MFs. Research in [11] compared the response of the system with different MFs and expressed the view that a triangular MF is better than other MFs. In fact, if one does not have priority for the MF shape, triangular or trapezoidal shapes are easy to implement and calculate quickly. However, if someone has certain priorities for their shapes (e.g., from histograms on sampled data), it may be interesting to create MFs with shapes derived from these apriori shapes after some smoothing, if necessary, according to [10]. In their research, paper [12] used the Gaussian membership function in the ANFIS model to test data on investment instruments. The fuzzy model is applied to the Tehran stock exchange index (TEPIX). The created model was able to predict the development of the stock index with high accuracy. A similar recommendation can be found in [13], which also recommends Gaussian membership functions. The authors further emphasize that this feature best reflects the nature of stock market data. [14] modified the conventional adaptive neuro-fuzzy inference model (ANFIS) using Gaussian functions of layer 4 membership instead of standard polynomial functions. The modified model achieved better computational performance for stock market prediction tasks. In contrast, in the study by [15] integrated technical indicators into the fuzzy system as input variables broken down into seven attributes, which they described using triangular fuzzy functions. Fuzzy sets are used to express ambiguities in the member function of fuzzy sets. However, so far, as follows from the above, there is no consensus on the geometry of the membership functions. These member functions are usually constructed using numeric data or a range of classes. However, there is uncertainty about the form of membership, i.e., whether it is a function of membership in a triangle or a function of trapezoidal membership, or other. When creating any fuzzy model, strong emphasis should be placed on membership functions, which are neglected and insufficiently researched in most re search papers, which can cause significant problems with the functionality of the model. The expression of membership functions depends on both the subject (how deep the researcher's experience is) and the context (where the problem is). For this reason, the present research focuses on the use of membership functions with varying degrees of uncertainty, which is gradually increasing. Furthermore, not only one type of membership function is used, as is usual in the vast majority of studies, but triangular, trapezoidal, Gaussian and bell membership functions are gradually created. Not to rely solely on one membership function is intentional, because in addition to the knowledge base, membership functions are essential for the proper functioning of the model. Incorrect description of input data through membership functions can provide incorrect outputs. Especially when applied to the stock market, incorrect description of fuzzy functions can cause significant differences in the profitability of the investment strategy and can cause generating incorrect investment signals, which can cause losses to potential investors.