The COVID-19 pandemic has been an extraordinary event, the type of event that rarely occurs but that has major impacts on the stock market. The pandemic has created high volatility and caused extreme fluctuations in the stock market. The stock market can be characterized as either linear or nonlinear. One method that can detect extreme fluctuations is extreme value theory (EVT). This study employed a semisystematic literature review on the use of the EVT method to estimate investment risk in the stock market. The literature used was selected by applying the preferred reporting items for systematic review and meta-analyses (PRISMA) guidelines, sourced from the ScienceDirect.com, ProQuest, and Scopus databases. A bibliometric analysis was conducted to determine the study characteristics and identify any research gaps. The results of the analysis show that studies on this topic are rarely carried out. Research in this field is generally performed only in univariate cases and is very complicated in multivariate cases. Given these limitations, further research could focus on developing a conceptual model that is dynamic and sensitive to extreme fluctuations, with multivariable inputs, in order to predict investment risk. The model developed here considered the variables that affect stock price fluctuations as the input data. The combination of VaR–EVT and machine-learning methods is effective in increasing model accuracy because it combines linear and nonlinear models.

Grey model GM(1,1) has attained excellent prediction accuracy with restricted data and has been broadly utilized in a range of areas. However, the GM(1,1) forecasting model sometimes yields large forecasting errors which directlyaffect the simulation and prediction precision directly. Therefore, the improvement of the GM(1,1) model is an essential issue, and the current study aims to enhance the prediction precision of the GM(1,1) model. Specifically, in order to improve the prediction precision of GM(1,1) model, it is necessary to consider improving the initial condition in the response function of the model. Consequently, the purpose of this paper is to put forward a new method to enhance the performance of the GM(1,1) model by optimizing its initial condition. The minimum sum of squared error was used to optimize the new initial condition of the model. The numerical outcomes show that the improved GM(1,1) model provides considerably better performance than traditional grey model GM(1,1) . The result demonstrates that the improved grey model GM(1,1) achieves the objective of minimizing the forecast errors.

This paper presents the use of two artificial neural networks models, namely the multilayer feedforward neural network (MLFF) and the recurrent neural network (RNN) are applied for Malaysia’s load forecasting. For this purpose, a half hourly load data is divided equally into three distinct sets for training, validation and testing. We use backpropagation as the learning algorithm and the sigmoid function as the transfer function for both hidden land output layers. The forecasting performances of were compared between these two models. We use the sum squared error (SSE) as the measure of performance and the correlation coefficient r , as the measure of relationship between the actual and the predicted values. Results show that, multilayer feedforward neural network (MLFF) and recurrent neural network (RNN) have comparable accuracy but the sum squared error for multilayer feedforward neural network (MLFF) is lower, thus making it better model than recurrent neural network (RNN).

Dengue Hemorrhagic Fever (DHF) is a tropical infectious disease transmitted by the Aedes aegypti mosquito and exhibits seasonal patterns with periodic increases in cases throughout the year. The control of vector-borne diseases such as DHF is very important for strengthening public health resilience against climate change, in line with the Sustainable Development Goals (SDGs) for Good Health, Well-being, and Climate Action. Therefore, this study was focused on Bogor city, which experiences high rainfall and continues to face an elevated risk of DHF. The objective was to develop a time series forecasting model to predict DHF outbreaks using Singular Spectrum Analysis (SSA). This is a statistical method for identifying patterns in time series data. Lunar and Solar calendars were adopted to capture seasonal patterns and determine the optimal window length for prediction. The results showed that the Lunar calendar more accurately captured local seasonal variation related to DHF risk. Moreover, the SSA model with one component and a window length of 7 achieved the best performance with a Mean Absolute Percentage Error (MAPE) of 0.0757. The forecast accuracy decreased with longer horizons, but the model provided reliable predictions for short-term periods (approximately 1 month, i.e., up to 4 weeks ahead), which were considered useful for planning DHF mitigation. The results emphasized that the combination of SSA with appropriate calendar systems could improve the accuracy of epidemiological predictions and support vector control policymaking in tropical regions.

The rising cost of medical insurance in the United States requires a thorough understanding of the factors influence it. Many factors can affect the cost of medical insurance, including age, sex, BMI, smoking habits and number of children. Problems arise when analysing data that contain outliers, as individual observations can a large impact on results. Robust regression is one of the useful methods in decreasing the effect of outliers in modelling. Hence, this paper aims to determine the best estimator between three estimators and to test the robustness of the best estimator when the data contaminated with outliers. We then applied to the dataset collected from the US Census Bureau published by Brett Lantz in 2013. The findings showed that R2 of LTS-estimator, MM-estimator and S-estimator were 0.9813, 0.6735 and 0.9728 respectively. When the data was contaminated with 10%, 20% and 30% of outliers the R2 values of LTS-estimator were 0.9399, 0.9030 and 0.8678. Thus, it can be concluded that the LTS-estimator can help in producing results that are resistant to outliers.

Government Purchasing Price (Harga Pembelian Pemerintah, HPP) is a policy established to maintain stable Harvested Dry Paddy (Gabah Kering Panen, GKP) prices at the farm-gate level and to protect farmers from declining incomes due to price drops during harvest periods. The effectiveness of the policy has yet to be evaluated; however, reports indicate that paddy prices in several regions are still below the HPP rate. This study explores variations in the trends and volatility of farm-gate-level GKP prices before and after the adoption of the new HPP policy and constructs a provincial-level forecasting model based on the Extreme Gradient Boosting (XGBoost) methodology using a daily provincial panel dataset covering the period from 1 January 2023 to 31 December 2025. An analysis of six sample provinces was performed: the Special Region of Yogyakarta (DIY), East Java, South Kalimantan, Bali, West Sumatra, and Jambi. The model was trained using pre-policy observations and recursively forecasted post-policy prices under a hypothetical no-HPP-policy scenario, which were then descriptively compared with observed prices after the policy was implemented. The results show that the model delivers very high prediction accuracy, with tested Mean Absolute Percentage Error (MAPE) values ranging from 0.61% to 1.60% and Root Mean Squared Error (RMSE) values ranging from IDR 50.31 to IDR 158.04. The comparison shows that observed post-policy GKP prices tend to remain higher and more stable over time than those forecasted under the no-HPP-policy scenario, although the magnitude of this difference varies among regions. These findings provide descriptive forecasting evidence regarding post-policy GKP price dynamics rather than definitive causal estimates of policy impact.