Forecasting Based on Fuzzy Logic of the Level of Epidemiological Risk for the Mexican State of Tamaulipas

Nowadays, in Mexico there exists a traffic light monitoring system to regulate the use of public space according to the risk level of infection with SARS‒CoV‒2. The monitoring system is applied to each state in Mexico and consists of four levels of risk encoded with four colors: green, yellow, orange and red. In this chapter we propose a Fuzzy Time Series Model to forecast the next color to be assigned to the Mexican state of Tamaulipas based on historical data from the monitoring system. We conducted a computational experiment to measure the accuracy of the model. The model accuracy was measured by the well‒known Root Mean Square Error (RMSE) index. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Fuzzy Time-Series Models Based on Intuitionistic Fuzzy, Rough Set Fuzzy, and Differential Evolution

Time-series forecasting is a vital concern for any data having temporal variations. Comparing with the other conventional time-series methodologies, the fuzzy time-series (FTS) proved its superiority. Substantial research using time-series forecasting to predict the stock index data has been found in the earlier works. The fuzzy sets approach alone cannot explain the data thoroughly. In this article, we have proposed three different methods of time-series forecasting. The first method is based on a rough set of FTS, a rule induction-based method;the second method is based on intuitionistic FTS. The last method is the extension of the second method using differential evolution. In the first model, a fuzzy algorithm based on rules is used to derive prediction rules from the time-series data and adopt an adaptive expectation model that replaces the fuzzy logical relationships or groups. In the second method, to split the universe of discourse into a non-uniform interval, a clustering algorithm-based intuitionistic fuzzy approach is used, taking care of the membership and non-membership function. Finally, the last method has been tuned for a better outcome using differential evolution. To examine the results, contrast analyses on the Taiwan stock exchange data and daily cases of COVID-19 pandemic prediction have been carried out. The outcome of the proposed approaches validates that the first and second techniques, showing promising results. However, the third method outperforms the other methods and the present techniques concerning the root-mean-square error metric. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.