Search

An important way to protect public health is to forecast air quality by giving early warnings about air pollution. Management of air quality is highly dependent on data from time series collected at air monitoring stations. However, data input must be processed first in time series observations for several reasons, such as measurement errors, to prevent misleading results during forecast analysis. Data obtained from different measurements brings a degree of uncertainty that contributes to uncertainty in the data. Therefore, by constructing the Fuzzy Symmetry Triangular Fuzzy number data using the standard deviation and percentage error methods, the procedure for preparing data containing uncertain information is presented in this paper. ARIMA then uses the pre-processed data to create a predictive model. The findings of this study suggest that smaller prediction errors have resulted in the use of the proposed method to create fuzzy numbers for ARIMA. This implies that, with fewer prediction errors, the accuracy of the prediction model is higher. [ABSTRACT FROM AUTHOR]

The basic goal of time series modeling is to collect and analyze historical time-series observations to build a model that accurately describes series trends. More attention is required while fitting this newly developed forecasting model to a timely data set. The fitting of the model is very important because it allows the model to capture complex interactions between its parameters, which allows for better prediction results. Hence, this research provides a systematic ARIMA model fitting procedure. The proposed model fitting procedure is very important during the data preparation phase as it can eliminate unreliable models. The result shows that the fitting in ARIMA model buildings has a significant impact. When the experimental results and performance were compared with the benchmark results, it was found that the proposed model showed better performance in terms of accuracy. As a result, the predictive accuracy of ARIMA fitting techniques can be improved. [ABSTRACT FROM AUTHOR]