Application of Singular Spectrum Analysis in Predicting Rupiah Exchange Yuan

Improving forecasts, particularly the accuracy, efficiency, and precision of time-series forecasts, is becoming critical for authorities to predict, monitor, and prevent the spread of the Coronavirus disease. However, the results obtained from the predictive models are imprecise and inefficient because the dataset contains linear and non-linear patterns, respectively. Linear models such as autoregressive integrated moving average cannot be used effectively to predict complex time series, so nonlinear approaches are better suited for such a purpose. Therefore, to achieve a more accurate and efficient predictive value of COVID-19 that is closer to the true value of COVID-19, a hybrid approach was implemented. Therefore, the objectives of this study are twofold. The first objective is to propose intelligence-based prediction methods to achieve better prediction results called autoregressive integrated moving average-least-squares support vector machine. The second objective is to investigate the performance of these proposed models by comparing them with the autoregressive integrated moving average, support vector machine, least-squares support vector machine, and autoregressive integrated moving average-support vector machine. Our investigation is based on three COVID-19 real datasets, i.e., daily new cases data, daily new death cases data, and daily new recovered cases data. Then, statistical measures such as mean square error, root mean square error, mean absolute error, and mean absolute percentage error were performed to verify that the proposed models are better than the autoregressive integrated moving average, support vector machine model, least-squares support vector machine, and autoregressive integrated moving average-support vector machine. Empirical results using three recent datasets of known the Coronavirus Disease-19 cases in Malaysia show that the proposed model generates the smallest mean square error, root mean square error, mean absolute error, and mean absolute percentage error values for training and testing datasets compared to the autoregressive integrated moving average, support vector machine, least-squares support vector machine, and autoregressive integrated moving average-support vector machine models. This means that the predicted value of the proposed model is closer to the true value. These results demonstrate that the proposed model can generate estimates more accurately and efficiently. Compared to the autoregressive integrated moving average, support vector machine, least-squares support vector machine, and autoregressive integrated moving average-support vector machine models, our proposed models perform much better in terms of percent error reduction for both training and testing all datasets. Therefore, the proposed model is possibly the most efficient and effective way to improve prediction for future pandemic performance with a higher level of accuracy and efficiency.

Rainfall is the height of rainwater that falls on a flat area, assuming it doesn't evaporate, doesn't seep, and doesn't flow. Rain levels are measured in mm (millimeters). The target of the research being conducted is in Badung Regency, Bali because Bali is a tourist area that is often visited by tourists and from Indonesian itself, so predictions of meteorology, such as rainfall will greatly impact tourism. In this test, predictions use the Long Short Term Memory (LSTM) method, using daily weather data from the BMKG from 2010 to 2020 as training data and daily weather data for 2021 as prediction data. Based on the test results above, the results show that the two LSTM tests with LSTM Model 128.64 and LSTM Model 64.32 have low MAE and MAPE error values. From First Scenario, the Mean Absolute Error (MAE) value is 8.97246598930908 and Mean Absolute Percentage Error (MAPE) value is 1.7657206683278308%. From Second Scenario, the Mean Absolute Error is 9.706669940783014 and Mean Absolute Percentage Error is 1.9028466692362323%. From the MAE and MAPE values obtained in these two scenarios, it can be proven that from the evaluation results of Rainfall predictions in Badung Regency, Bali, the predictions can be said to be very accurate because they have an error value of less than 10.

Over the years, the United States Dollar, European Euro, and the British Pound Sterling exchange rate to Nigerian Naira has been on the increase. It has become pertinent to identify robust models that will help to cope with the variability associated with the increase in exchange rate. Several studies showed the ARIMA method to be highly useful in modelling and forecasting exchange rates. However, not much work has been done on modelling and forecasting Nigerian Exchange rate using machine learning models which is the focus for this study. The models we used in the study are; the Autoregressive Integrated Moving Average (ARIMA), Artificial Neural Network (ANN), and ARIMA-ANN models. Secondary data obtained from Central Bank of Nigeria (CBN) were used. The results showed that the most appropriate model out of the three time series models considered for these exchange rates is the ARIMA-ANN which produced a better forecast compared to ARIMA and ANN. This conclusion was based on the lowest standards of prediction accuracy which ARIMA-ANN produced the lowest Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE) values for the three different currencies we compared against the naira. Based on the continuous increase in the Nigerian Exchange Rate, the Government and policymakers should take economic measures to avoid the persistent downfall of the Nigerian Naira.

This study aims to design, implement, and compare the performance of the Backpropagation algorithm from Artificial Neural Networks and the Seasonal Autoregressive Integrated Moving Average (SARIMA) model in predicting the optimal daily rice requirement at Grillme Restaurant in Pontianak. The main problem faced by the restaurant is the uncertainty in determining the required daily rice stock, which periodically results in either understocking (shortage) or overstocking (wastage), leading to operational losses. To address this, the study utilizes historical daily rice sales data from January 2023 to April 2025 as the database for training and testing both predictive models. The SARIMA approach is employed to capture time series components (trend and seasonality), while Backpropagation is utilized to model non-linear patterns. Comparative test results indicate that the SARIMA model achieved superior accuracy compared to the Backpropagation model. This is confirmed by the Mean Absolute Percentage Error (MAPE) value of the SARIMA algorithm being 17.35%, which is lower than the MAPE value of Backpropagation at 19.62%. The MAPE values obtained by both models demonstrate good predictive capability, but it is concluded that SARIMA is more recommended for a more efficient and planned management of rice stock at Grillme Restaurant in Pontianak.

Stocks have high-profit potential but also have high risk. Many people have ways to forecast stock prices. The Geometric Brownian Motion (GBM) method forecasts stock prices. The data used in this study are closing stock price data from July 1, 2021 to August 31, 2021 taken from Yahoo! Finance. The stocks used in this research are Bank Rakyat Indonesia (BBRI), Indofood Sukses Makmur (INDF), and Telkom Indonesia (TLKM). A strategy is carried out to improve prediction accuracy by utilising the Kalman Filter (KF). This research will compare the mean absolute percentage error (MAPE) value between GBM-KF, which was manually computed and computed using the Python library. As an example of this research, for BBRI stock, the high GBM MAPE value of 9.02% can be reduced to 3.52% with manually computed GBM-KF and 3.68% with Python library computed GBM-KF. Similarly, INDF and TLKM stocks are showing a significant reduction in MAPE values to deficient levels in some cases. The GBM-KF method employing manual computing may enhance the overall precision of stock price forecasting. Future research may enhance this study by using the GBM-KF model on alternative financial instruments, integrating supplementary market data, or evaluating its efficacy under extreme market conditions.

This study focuses on the design and optimization of consumer behavior prediction models in online e-commerce reviews. To address the issues of slow convergence and insufficient robustness in the traditional Q-learning reinforcement learning algorithm, this article introduces a probabilistic action-selection algorithm. This algorithm employs a multi-step, iterative mechanism that uses instantaneous differencing to increase the likelihood of selecting high-Q actions during model iteration, thereby accelerating the solution process and ensuring robust network optimization. Given the nonlinear and high-noise characteristics of consumer behavior time-series data in e-commerce reviews, we propose a hybrid intelligent prediction model, Q-learning-Artificial Neural Network-Hidden Markov Model (QL-ANN-HMM), that effectively reduces the impact of systematic random errors and significantly improves prediction accuracy. Experimental results demonstrate that the improved Q-learning algorithm achieves 2.71% and 5.96% improvements in mean absolute percentage error (MAPE) and normalized mean squared error (NMSE), respectively, compared to the traditional Q-learning algorithm on the Amazon Reviews 2023 and Flipkart Reviews datasets. Additionally, the QL-ANN-HMM model achieves lower mean absolute error (MAE), MAPE, and NMSE values on both datasets, recorded at 0.0195, 0.019, and 0.0189, respectively. This research not only provides novel theoretical support and technical methods for predicting consumer behavior in online e-commerce reviews but also enables e-commerce platforms to more accurately track market dynamics, optimize resource allocation, and achieve sustainable development by comprehensively analyzing consumer behavioral data.

This study aims to create a monthly sales quantity budget by making use of the previous income data of an enterprise operating within the construction sector, which is considered the locomotive of the economy. For estimating time-series of sales as a linear model ARIMA (Auto-Regressive Integrated Moving Average), as nonlinear model LSTM (Long ShortTerm Memory) and a HYBRID (LSTM and ARIMA) model built to improve system performance compared to a single model was used. As a result of the study, Mean Square Error (MSE), Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE) and Mean Absolute Error (MAE) values obtained from each of the methods used in the application were compared, and a monthly sales volume budget was created for 2017 with all the methods used. When the MAPE and MSE values obtained from each of these methods were compared, the best performance was the Hybrid model that gave the lowest error, and in addition, the fact that all of the application models got very realistic results by using the historical data showed the success of the predictions.

Drought is a complex and frequently occurring natural hazard in many parts of the world. Therefore, accurate drought forecasting is essential to mitigate its adverse impacts. This research has inferred the implication and the appropriateness of the extreme learning machine (ELM) algorithm for drought forecasting. For numerical evaluation, time series data of the Standardized Precipitating Temperature Index (SPTI) are used for nine meteorological stations located in various climatological zones of Pakistan. To assess the performance of ELM, this research includes parallel inferences of multilayer perceptron (MLP) and autoregressive integrated moving average (ARIMA) models. The performance of each model is assessed using root mean square error (RMSE), mean absolute error (MAE), mean absolute percent error (MAPE), Kling‐Gupta efficiency (KGE), Willmott index (WI), and Karl Pearson’s correlation coefficient. Generally, graphical results illustrated an excellent performance of the ELM algorithm over MLP and ARIMA models. For training data of SPTI‐1, ELM’s best performance has observed at Chitral station (RMSE = 0.374, KGE = 0.838, WI = 0.960, MAE = 0.272, MAPE = 259.59, R = 0.93). For SPTI‐1 at Astore station, the numerical results are (RMSE = 0.688, KGE = 0.988, WI = 0.997, MAE = 0.798, MAPE = 247.35). The overall results indicate that the ELM outperformed by producing the smallest RMSE, MAE, and MAPE values and maximum values for KGE, WI, and correlation coefficient values at almost all the selected meteorological stations for (1, 3, 6, 9, and 12) month time scales. In summary, this research endorses the use of ELM for accurate drought forecasting.

Attention mechanism is useful in spatio-temporal wind speed prediction: Evidence from China

Fisheries is one of the sources of food security in Indonesia which plays an important role in the rich biodiversity of Indonesian coastal waters. This research uses the Chen Fuzzy Time Series Model forecasting method. The samples used in this study are data on the number of fishery commodity products with specific Tilapia and Catfish monthly Aceh Province as much as 24 data, starting from January 2021 to December 2022. The results of forecasting the number of tilapia in January 2023 were 1,320,815, with a MAPE (Mean Absolute Percentage Error) value of 12.93%. And the result of forecasting the number of catfish in January 2023 was 2,339,568, with a MAPE (Mean Absolute Percentage Error) value of 22.9%. This shows that forecasting using the Chen model Fuzzy Time Series method is good enough to forecast the number of tilapia and catfish in Aceh Province.

Integration of weather forecast and artificial intelligence for a short-term city-scale natural gas consumption prediction

Background: Dengue hemorrhagic fever occurs through the bite of Aedes mosquitoes, primarily Aedes aegypti, carrying dengue viruses. In recent decades, the risk increased dramatically, not only in the tropics but also in subtropical regions.Objective: This study aimed to determine the best model for forecasting dengue hemorrhagic fever prevalence in Sulawesi Tenggara, Indonesia.Method: This was a retrospective analytical study using secondary data from the Sulawesi Tenggara Provincial Health Office from 2014 to 2019. ARIMA model was used for data analysis.Results: ARIMA (0.1.1)(0.1.1)4 was selected as the best-suited model. Based on the forecast, there would be an increase in dengue hemorrhagic fever prevalence over the next two years, with a mean absolute percentage error value of 4.41%.Conclusion: Forecasting results indicated that the peaks of dengue hemorrhagic fever cases would be in March, July, and November, and the increase will occur in the same months each year. Also, forecasting results were very good. Public health practitioners can use this model to prevent and eradicate dengue hemorrhagic fever. The ARIMA model would also be useful for nursing practice in caring for patients with dengue fever in the future.

East Java's macroeconomic stability is vulnerable to external shocks, particularly imported inflation, which impacts domestic price formation. This study aims to develop an inflation forecasting model using the AutoRegressive Integrated Moving Average with Exogenous Variables (ARIMAX) approach as an Early Warning System instrument for regional policymakers. This study uses monthly time series data for the 2015–2024 period. Exogenous variable selection was conducted through the Granger Causality Test, which showed that only import volume had a significant effect on inflation, while exchange rates and exports were insignificant. The estimation results show that the ARIMAX(1,0,1) model is the best model with a forecasting accuracy level based on the Mean Absolute Percentage Error (MAPE) value of 10.40%, which is categorized as good. East Java's inflation projection for the 2025–2029 period shows an increasing trend from 1.99% to 3.28%, indicating the need for vigilance for policymakers in strengthening import supply chain management to mitigate the risk of future price pressures. Based on the theoretical and empirical review, this study hypothesizes that the exchange rate and international trade indicators have a significant predictive relationship with the inflation rate in East Java Province. This study aims to develop an inflation forecasting model for East Java using the ARIMAX approach, considering the exchange rate and international trade variables as candidate exogenous variables.

Road inconsistency may cause dangerous driving experience that leads to carelessness while handling the vehicle. To determine the road inconsistency, development of models which are based on the 85th percentile of speed is adopted. The models which are taken into consideration are other geometric road characteristics such as superelevation, radius of curve, width of curve, tangent length, and road gradient that can help to minimize the risk of accidents at curve. To further analyze the effectiveness of these developed models, validation of the models using new set of data and comparing it with other established models is crucial. The validation process is conducted by calculating the mean squared error (MSE), mean absolute error (MAE), and mean absolute percentage error (MAPE). MSE value will indicate the differences between the empirical results with the predicted results, while MAE calculates the absolute difference between the empirical results and predicted results. MAPE evaluates the percentage errors between the empirical results with the predicted results. Small values of MSE, MAE, and MAPE is a proof that the models can predict good results.

Pangkep Regency is one of the regions in South Sulawesi that is the center of national salt production. Salt production in the area is still dependent on sea water evaporation so that rainfall is one of the determining factors for the success of salt productivity. Statistical downscaling is an accurate method for rainfall forecasting by linking the local scale rainfall in Pangkep Regency (response variable) with the global scale of the global circulation model/GCM output (predictor variable). However, the GCM output rainfall has a large dimension, which is an 8×8 grid (64 predictor variables), causing multicollinearity. The linearized ridge regression (LRR) method is used to overcome this problem. This method combines the performance of generalized ridge regression and Liu-type methods to reduce multicollinearity. In addition, dummy variables based on the K-means clustering technique were added to the model to overcome heteroscedasticity. The purpose of this study is to obtain the results of rainfall forecasting in Pangkep Regency using the LRR method in the statistical downscaling model. The model generated from the LRR method with dummy variables is better at explaining the variability of rainfall in Pangkep Regency. The value is higher (72%) than without dummy variables (57%). The addition of dummy variables in the LLR model has better accuracy in forecasting rainfall. The actual rainfall correlation of Pangkep Regency with has the largest correlation (0.76) with the smallest mean absolute percentage error value (0.49). The results obtained are that the months of May - November tend to have relatively low rainfall, so that salt farmers can produce salt with good quantity and quality.