Forecasting Inflation Based on Hybrid Integration of the Riemann Zeta Function and the FPAS Model (FPAS + ζ): Cyclical Flexibility, Socio-Economic Challenges and Shocks, and Comparative Analysis of Models

This research study focuses on forecasting the future values of peak demand in electricity consumption for Luzon, Philippines based on monthly historical data spanning from 2001 to 2020. The data was obtained from the official website of the Philippines Department of Energy (DOE). The primary objective of this study is to employ the ARIMA (Autoregressive Integrated Moving Average) model-building procedure developed by Box and Jenkins to accomplish accurate peak demand forecasting. The methodology involved conducting various tests and evaluations to identify the ARIMA model with the least Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE). After careful analysis, the best-fitting ARIMA model was determined to be ARIMA (11, 1, 12). The findings of this study indicate that, according to the ARIMA (11, 1, 12) model, Luzon's peak demand is projected to reach 10,497.65 megawatts by December 2021. Furthermore, the model predicts that by the end of 2022, 2023, and 2024, Luzon's peak demand will be approximately 10,738.34 MW, 10,953.98 MW, and 11,148.43 MW per electrical grid, respectively. The accuracy of the ARIMA (11, 1, 12) model is found to be satisfactory, with a low MAPE value of 3.639% and the most negligible RMSE value of 517.132. The implications of these forecasted peak demand values are significant for decision-makers in the energy and utilities sector. The accurate predictions provided by the ARIMA model can aid in resource allocation, infrastructure planning, and overall operational strategies to effectively meet the anticipated high-demand periods. In conclusion, this study successfully forecasts Luzon's future values of peak demand in electricity consumption using the ARIMA (11, 1, 12) model. The findings highlight the importance of accurate peak demand forecasting and provide valuable insights for energy industry professionals.

Short-term electricity demand forecasting with MARS, SVR and ARIMA models using aggregated demand data in Queensland, Australia

Forecasting plays a pivotal role in economic planning, particularly in aligning supply with demand and informing production decisions. This study aims to compare the performance of the Autoregressive Integrated Moving Average (ARIMA) and Seasonal ARIMA (SARIMA) models in forecasting the non-oil and gas export values of East Java, a region known for its dynamic trade activity. Using monthly time series data spanning from January 2007 to January 2024, sourced from the Central Statistics Agency (BPS) of East Java Province, this research conducts an in-depth analysis of forecasting accuracy and model suitability. Before model implementation, the dataset underwent several preprocessing steps to ensure its quality, including the handling of missing values and outlier adjustments. Both ARIMA and SARIMA models were developed, calibrated, and evaluated using standard forecasting performance metrics, namely Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE). The ARIMA model exhibited consistently lower error rates across all three metrics, indicating its robustness in capturing the underlying patterns within the export data. In contrast, while the SARIMA model incorporated seasonal components, its performance did not surpass that of ARIMA in this specific case. The comparative findings suggest that, despite the seasonal nature of trade, the ARIMA model is more suitable for short-term forecasting of East Java’s non-oil and gas exports. This research contributes to the broader literature on economic forecasting by emphasizing the importance of selecting appropriate models based on data characteristics. Furthermore, the results provide valuable insights for policymakers and stakeholders engaged in export planning and regional trade development In this result the ARIMA model overcome the SARIMA with MAPE 0.116 to 0.983.

In recent years, as global financial markets have become increasingly connected, the degree of correlation between financial assets has become closer, and technological advances have made the transmission of information faster and faster, and information networks have integrated capital markets into one, making it easier for single financial market risk problems to form systemic risk through a high degree of market linkage effects. Based on the characteristics of financial markets containing both linear and nonlinear components, this paper chooses to use Autoregressive Integrated Moving Average (ARIMA) model and feedback Support Vector Regression (SVR) models to effectively integrate the ARIMA model and the SVR model, taking into account their respective linear and nonlinear characteristics. The paper chooses to use the (Autoregressive Integrated Moving Average (ARIMA) model and feedback Support Vector Regression (SVR) models to effectively integrate the strengths of the ARIMA and SVR models in terms of linearity and nonlinearity to perform forecasting analysis of financial markets. One of the important functions of forecasting is to transform future uncertainty into measurable risk, so that we can base our plans and actions on it. In this paper, the combined ARIMA-SVR model is compared with the single ARIMA model and SVR model in terms of the mean absolute error (MAE), root mean square error (RMSE), and mean absolute percentage error (MAPE), where MAE and RMSE measure the absolute error between the predicted and true values, and MAPE measures the relative error between the predicted and true values. and the relative error between the true value. The results show that the combined ARIMA-SVR model has a better forecasting effect and higher forecasting accuracy than the single ARIMA model and SVR model, and the SVR model has higher forecasting accuracy than the ARIMA model in forecasting financial markets.

Introduction Drought is a critical climate condition affecting many places on Earth. Drought severity is often measured using a combination of different variables including rainfall, temperature, humidity, wind, soil moisture, and steam flow. During the last decades, Iran has suffered from drought conditions and it may suffer more in future. The frequent occurrence of drought in Iran is mainly due to lack of sufficient precipitation and improper water management system. Drought is often categorized into three types: meteorological, agricultural, and hydrological. There are various methods for measuring and quantifying drought severity. The most commonly used ones are Palmer Drought Severity Index (PDSI) and Standardized Precipitation Index (SPI). Remotely sensed data can also be used for monitoring drought condition. The most widely used ones are Normalized Difference Vegetation Index (NDVI), Land Surface Temperature (LST), Vegetation Condition Index (VCI), Temperature Vegetation Index (TVX) and NDVI deviation Index (DEV). Neural Network (NN) and Autoregressive Integrated Moving Average (ARIMA) are two of the most widely applied methods for modeling and monitoring drought severity indices. In this paper, monthly time series data (2000 to 2014) of three remotely sensed indices (i.e., NDVI, VCI, and TVX) and one meteorological index (i.e., SPI) were applied for modeling drought severity. In addition, the NN and ARIMA were developed for modeling these indices. Materials & Methods Data used in this paper were the time series of NDVI, VCI, TVX, and SPI. The study area in this paper was Arak, center of Markazi province. It has cold and wet winters with warm and dry summers. ARIMA and NN were employed for modeling indices. ARIMA model is generally derived from three basic time series models: Autoregressive (AR), Moving Average (MA), and Autoregressive Moving Average (ARMA). These basic models are used with static time series, i.e., they have constant mean and covariance in relation to time. Usually, NN method has three layers. The first layer or the input layer introduces data to network. Input data is processed in the second layer or the hidden layer. Finally, the output layer produces the results of the input data. In this paper, single hidden layer feed forward network, which is the most widely utilized NN form, was employed for modeling indices. Results & Discussion After implementing NN and ARIMA models on the time series data, the performance of the models was evaluated using Mean Absolute Error (MAE) and Root Mean Square Error (RMSE). The RMSE obtained by NN and used for modeling NDVI, VCI, TVX, and SPI indices of Arak were 0.1944, 0.2191, 0.1295, and 0.2990, respectively. In addition, RMSE obtained from ARMIA, and used for modeling these indices were 0.0770, 37.2318, 0.2658, and 1.3370. In another experiment, the correlation between remotely sensed indices and SPI was studied. Among the remotely sensed indices, TVX shows the most powerful correlation with SPI. Conclusion In the present study, drought condition in the central region of Markazi province was studied during the 2000 to 2014 period. We used the time series of remotely sensed data (such as LST and NDVI) and meteorological data (such as SPI). Then TVX, VCI, and DEV indices were extracted from NDVI and LST data. NN and ARIMA were applied for modeling time series data. Based on the findings, it is concluded that NN is more successful and efficient than ARIMA for this study area. In addition, TVX, which is built based on NDVI and LST, had the most powerful correlation with SPI. This issue implies that both vegetation index and temperature index had an important role in modeling and monitoring drought condition.

The Philippines is one of the fastest urbanizing countries in the East Asia and Pacific region (Baker & Watanabe, 2017). Despite having its advantages, urbanization still has its challenges that require extensive urban management and development programs for it to be prevented and minimized. In this paper, the researchers forecasted the urban population growth of the Philippines using the Autoregressive Integrated Moving Average (ARIMA) Model. The historical data obtained from the World Bank Group was from 1960 to 2020. The R Programming Language was used as the medium for the entire forecasting process. Autocorrelation Function (ACF) and Partial Autocorrelation Function (PACF) plots, Augmented Dickey-Fuller (ADF) test, Phillips-Perron (PP) test, and Kwiatkowski, Phillips, Schmidt, and Shin (KPSS) test were used for testing the stationarity of the time-series data. Moreover, Akaike Information Criteria (AIC), Corrected Akaike Information Criterion (AICc), and Schwarz Information Criteria (SIC) were used as criteria for selecting the best ARIMA model. It was shown that the best ARIMA model for forecasting the urban population growth of the country is ARIMA (20, 1, 10). This model has been formulated and chosen through the mentioned statistical tests, and criteria for validation, and was further validated using error measures. The chosen ARIMA model was proven to be accurate based on the Root Mean Square Error (RMSE) of 0.18877 and the Mean Absolute Percentage Error (MAPE) of 3.71%. The researchers found an increase in the trend of 1.95% by 2022, 2.08% by 2024, 2.19% by 2026, and 2.36% by 2028. This potential rise in urban population growth in the Philippines may improve the economy of the country for the next 6 years, but this could also imply that the underlying issues of urbanization may get worse. The researchers conclude that the Philippine national government and local government units should have better and strengthened urban management and development programs to aid these problems. Government officials and even private sectors may use this paper as a reference to have an informed decision and policy-making. KEYWORDS: Autoregressive Integrated Moving Average (ARIMA) Model, Box-Jenkins Method, Urbanization, Urban Population Growth, Forecast, R Programming Language

Weather forecasting is an interesting research problem in flight navigation area. One of the important weather data in aviation is visibility. Visibility is an important factor in all phases of flight, especially when the aircraft is maneuvering on or close to the ground, i.e., during taxi-out, take-off and initial climb, approach and landing and taxi-in. The aim of these study is to analyze intermediate variables and do the comparison of visibility forecasting by using Autoregressive Integrated Moving Average (ARIMA) and Long Short Term Memory (LSTM) Model. This paper proposes ARIMA model and LSTM model for forecasting visibility at Hang Nadim Airport, Batam Indonesia using one variable weather data as predictor such as visibility and combine with another variable weather data as moderating variables such as temperature, dew point and humidity. The models were tested using weather time series data at Hang Nadim Airport, Batam Indonesia. This research compares the Root Mean Square Error (RMSE) resulted by LTSM model with the RMSE resulted by ARIMA model. The results of this experiment show that LSTM model with/or without intermediate variable has better performance than ARIMA Model.

This book chapter compares the out-of-sample forecast for inflation rates in Nigeria using ARIMAX and ARIMA models. To achieve this, Annual Data on Exchange Rate, Inflation Rate, Interest Rate and Unemployment Rate from 1981 to 2017 was sourced from Central Bank of Nigeria (CBN). The analysis used data from 1981 to 2010 while 2011 to 2017 was used to valid the forecast from the ARIMA and ARIMAX models. The preliminary analysis revealed that natural log transform of inflation rate is normally distributed and stationary at first difference while Exchange Rate, Inflation Rate, Interest Rate and Unemployment Rate were used as exogenous variables in the ARIMAX models. The following models ARIMA(1,1,0), ARIMA(1,1,1), ARIMA(0,1,1), ARIMAX(1,1,0), ARIMAX(1,1,1) and ARIMAX(0,1,1) were compared for both in-sample and out-of-sample forecasts. Using the Root Mean Square Error (RMSE) as selection criteria, ARIMAX(0,1,1) with RMSE of 0.6810 emerged as superior model for the in-sample forecast for forecasting inflation rate in Nigeria while ARIMA(1,1,1) emerged as a superior model for the out-of-sample forecast for inflation rate in Nigeria and its forecast for inflation revealed a negative growth in inflation in Nigeria. This study therefore recommended ARIMA(1,1,1) model be used for out-of-sample forecast for inflation rate in Nigeria.

Learned prediction of cholesterol and glucose using ARIMA and LSTM models – A comparison

In this study, the performance of Autoregressive Integrated Moving Average (ARIMA) and Artificial Neural Networks (ANNs) models was investigated and evaluated using daily confirmed cases of COVID-19 in Nigeria. The stationarity status of the data collected was established using Augmented Dickey Fuller unit root test. The residual normality test was also carried out with the residual plots indicating adequacy of the fitted ARIMA model. The results of neural networks were analyzed using back-propagation for multilayer feed-forward powered by sigmoid function. Utilizing backpropagation method based on three factors expressed in terms of the learning rate, the distance between the actual output and predicted output and the activation function, the network weights were generated The performance indices for ARIMA and ANNs models were evaluated using Mean Square Error (MSE), Root Mean Square Error (RMSE) and Mean Absolute Error (MAE and the results revealed that the ARIMA model performed better than the ANN model considering the minimum prediction error and forecasting ability. The ARIMA (2, 1, 1) model appeared to be the best fitted model over the ANN model for the daily confirmed covid-19 cases considered.

The global coffee market is booming and consumer preferences are refined. Vending machines have significant potential in coffee sales. However, predicting coffee sales to balance supply and demand, reduce inventory, and improve customer satisfaction is a crucial issue for the industry. This paper presents a study on the prediction of coffee sales in vending machines using the Autoregressive Integrated Moving Average (ARIMA) model. The analysis is based on a comprehensive dataset of daily coffee sales records, which provides valuable insights into consumer behavior and sales trends. The ARIMA model is employed to capture the temporal dependencies and patterns within the sales data. The fitted model is then validated using the Ljung-Box Q-test to ensure that the residuals are uncorrelated, indicating a good fit. The results show that the ARIMA model has a high prediction accuracy, with a Root Mean Square Error (RMSE) of 68.9416, and can roughly predict future coffee sales. This study contributes to the literature on time series analysis and forecasting in the retail industry, particularly in the context of automated vending machines. The findings have practical implications for vending machine operators, who can leverage the predictions to optimize inventory management, sales strategies, and customer service.

BackgroundMany studies have compared the performance of time series models in predicting pulmonary tuberculosis (PTB), but few have considered the role of meteorological factors in their prediction models. This study aims to explore whether incorporating meteorological factors can improve the performance of time series models in predicting PTB.MethodsWe collected the monthly reported number of PTB cases and records of six meteorological factors in three cities of China from 2005 to 2018. Based on this data, we constructed three time series models, including an autoregressive integrated moving average (ARIMA) model, the ARIMA with exogenous variables (ARIMAX) model, and a recurrent neural network (RNN) model. The ARIMAX and RNN models incorporated meteorological factors, while the ARIMA model did not. The mean absolute percentage error (MAPE) and root mean square error (RMSE) were used to evaluate the performance of the models in predicting PTB cases in 2018.ResultsBoth the cross-correlation analysis and Spearman rank correlation test showed that PTB cases reported in the study areas were related to meteorological factors. The predictive performance of both the ARIMA and RNN models was improved after incorporating meteorological factors. The MAPEs of the ARIMA, ARIMAX, and RNN models were 12.54%, 11.96%, and 12.36% in Xuzhou, 15.57%, 11.16%, and 14.09% in Nantong, and 9.70%, 9.66%, and 12.50% in Wuxi, respectively. The RMSEs of the three models were 36.194, 33.956, and 34.785 in Xuzhou, 34.073, 25.884, and 31.828 in Nantong, and 19.545, 19.026, and 26.019 in Wuxi, respectively.ConclusionsOur study revealed a possible link between PTB and meteorological factors. Taking meteorological factors into consideration increased the accuracy of time series models in predicting PTB, and the ARIMAX model was superior to the ARIMA and RNN models in study settings.

Abstract: Malang is known as a student city since there are a lot of schools and universities that can be found in Malang Indonesia. Malang is also an attractive tourist place with many tourist attractions in the city of Malang. Public transportation in the city of Malang is also very varied, ranging from conventional and based online. Access to the city of Malang is varied, namely trains, buses, and planes. Thus economic growth in the city of Malang is getting better, this can be seen from the economic activity in the increasingly crowded city of Malang. A good economy is usually followed by stable inflation. For this reason, it is necessary to examine how the monthly inflation rate in Malang city. This study aims to forecast inflation in the coming periods using the Autoregressive Integrated Moving Average (ARIMA) model. Secondary monthly inflation data is obtained from BPS Malang. From this research, the ARIMA model (2,0,3) is obtained. The accuracy model is used in this research namely root means square error (RMSE), mean absolute error (MAE), and mean absolute square error (MASE). The accuracy value is RMSE equal 0.2645467, MAE equal 0.2013898, and MASE equal 0.6047399. Keywords: Monthly inflation forecasting, BPS Malang city, ARIMA model.

Predicting stock market prices accurately is a major task for investors and traders seeking to optimize their decision-making processes. This research focuses on the comparative analysis of advanced machine learning (ML) techniques, particularly, the Long Short-Term Memory (LSTM) model and Autoregressive Integrated Moving Average (ARIMA) model for predicting stock market prices. The study enforces thorough data collection and preprocessing to ensure the quality and reliability of the historical stock price data, forming a robust foundation for the predictive models. The core contribution of this paper lies in its systematic and comparative analysis of these two models. A range of performance metrics, including Mean Absolute Error (MAE) and Root Mean Square Error (RMSE), are employed to assess and contrast the predictive accuracy and efficiency of the LSTM and ARIMA models. The research findings indicate that the ARIMA model, contrary to expectations, outperforms the LSTM model in this study, achieving lower RMSE and MAE values. Specifically, the ARIMA model demonstrates a Test RMSE of 4.336 and a Test MAE of 3.45926, indicating its superior predictive accuracy compared to the LSTM model. Furthermore, the study sets its findings against the backdrop of existing literature by comparing the performance of its models with those reported in previous research. This comparison shows better results achieved by our stock market prediction models. By addressing limitations observed in prior studies and demonstrating practical applicability, this research contributes to advancing stock market prediction methodologies, offering valuable insights for investors and traders.

Using weather factors and google data to predict COVID-19 transmission in Melbourne, Australia: A time-series predictive model