Moving Average Model Research Articles

Analyzing the Selective Stock Price Index Using Fractionally Integrated and Heteroskedastic Models

Stock market indices are important tools to measure and compare stock market performance. The Selective Stock Price (SSP) index reflects fluctuations in a set value of financial instruments of Santiago de Chile’s stock exchange. Stock indices also reflect volatility linked to high uncertainty or potential investment risk. However, economic shocks are altering volatility. Evidence of long memory in SSP time series also exists, which implies long-term persistence. In this paper, we studied the volatility of SSP time series from January 2010 to September 2023 using fractionally heteroskedastic models. We considered the Autoregressive Fractionally Integrated Moving Average (ARFIMA) process with Generalized Autoregressive Conditional Heteroskedasticity (GARCH) innovations—the ARFIMA-GARCH model—for SSP log returns, and the fractionally integrated GARCH, or FIGARCH model, was compared with a classical GARCH one. The results show that the ARFIMA-GARCH model performs best in terms of volatility fit and predictive quality. This model allows us to obtain a better understanding of the observed volatility and its behavior, which contributes to more effective investment risk management in the stock market. Moreover, the proposed model detects the influence volatility increments of the SSP index linked to external factors that impact the economic outlook, such as China’s economic slowdown in 2012 and the subprime crisis in 2008.

Investigation of the solubility behavior of emodin and aloe-emodin in water + ethanol mixtures at various temperatures ranging from 283.15 K to 323.15 K

A thermostated reactor and UV–visible spectrophotometer analysis were used to determine the solubility of emodin and aloe-emodin, two naturally occurring compounds with pharmacological activity. At a pressure of 0.1 MPa under atmospheric conditions, and temperatures from 283.15 to 323.15 K with intervals of 5 K, the solubility in different water–ethanol combinations was the primary focus of the investigation. Under constant solvent composition, a positive correlation was observed between the solubility of emodin/ or aleo-emodin and the experimental temperature in all examined mixed solvent systems. To better understand the relationships between solutes and solvents and the interactions between several solvents, the KAT-LSER model was used to conduct multiple linear regression analysis on systems containing either pure solvents or a combination of solvents. In addition, several thermodynamic models were used to establish a connection with the empirical solubility data. These models included the Jouyban-Acree, Apelblat-Jouyban-Acree, van’t Hoff-Jouyban-Acree, and Ma models. The correlation findings demonstrate that all four thermodynamic models accurately predicted emodin and aleo-solubility emodin’s behavior under different solvent and temperature settings. Furthermore, the thermodynamic characteristics of emodin solubility in binary solvent combinations were determined using the van’t Hoff model. According to the findings, two compounds were found to undergo endothermic and non-spontaneous dissolution.

Carbon Emission Prediction Algorithm for Community Electricity Consumption Based on Improved PSO Algorithm with Optimized Autoregressive Moving Average Models

Abstract The anticipation of carbon emissions stemming from community electricity usage stands as a pivotal field of study. Such analysis holds the potential to furnish communities with informed pathways towards crafting viable carbon reduction strategies, thereby contributing significantly to the overarching goals of carbon peaking and eventual neutrality. The intricacies of carbon emissions from community electricity consumption are manifold, entangled with variables like energy consumption patterns, energy mix, and carbon emission coefficients. Hence, the imperative lies in crafting a predictive framework adept at holistically integrating these variables to enhance prognostic precision and reliability. Within this research, we propose a dynamic carbon emission factor regression model. This model is uniquely poised to capture real-time shifts influenced by changes in energy structure and policy landscapes, thereby amplifying predictive sensitivity. Leveraging the Particle Swarm Optimization (PSO) algorithm, we synchronously optimize the autoregressive terms (p) of the Autoregressive Integrated Moving Average (ARIMA) model and the moving average terms (q) of the MA model to attain a globally optimal solution. Crucially, this approach obviates the need for manual intervention and arbitrary parameter selection. In contrast to conventional optimization methodologies, our paper advances the PSO’s weight calculation mechanism. By assigning greater weight values during the initial iterations, the algorithm maintains robust global search capabilities. Subsequently, the inertia weight diminishes progressively throughout the iteration process, fostering precise local exploration.

Foreign Exchange Rates Prediction Based on Comparative Models

With the development of globalization, fluctuations in the foreign exchange market have the huge potential to influence global economy. Machine Learning technology shows great potential in the field of financial forecasting than traditional statistical methods in recent years. This research selected statistical model Autoregressive Integrated Moving Average (ARIMA) and machine learning models Linear Regression and Long- and Short-Term Memory (LSTM) to make training and testing of historical data on the forex market. Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and R-Squared were chosen as evaluation indexes to evaluate the performance of each model and compare their performance in forex exchange rate forecasts. The results show that ARIMA and LSTM could make better predictions than Linear Regression, between which LSTM has higher degree of fitting data. This research provides a detailed analysis comparing the performance of different models in forex exchange rate forecasting whose results are beneficial to guide forex market forecast in practical application and provide reference for future research.

Epidemiological characterization of hand, foot, and mouth disease among hospitalized children from 2014 to 2023 in a hospital in Henan Province: Longitudinal surveillance study.

Hand, foot, and mouth disease (HFMD) is an acute infectious illness primarily caused by enteroviruses. The present study aimed to describe the epidemiological characteristics of hospitalized HFMD patients in a hospital in Henan Province (Zhengzhou, China), and to predict the future epidemiological parameters. In this study, we conducted a retrospective analysis of general demographic and clinical data on hospitalized children who were diagnosed with HFMD from 2014 to 2023. We used wavelet analysis to determine the periodicity of the disease. We also conducted an analysis of the impact of the COVID-19 epidemic on the detection ratio of severe illness. Additionally, we employed a Seasonal Difference Autoregressive Moving Average (SARIMA) model to forecast characteristics of future newly hospitalized HFMD children. A total of 19 487 HFMD cases were included in the dataset. Among these cases, 1515 (7.8%) were classified as severe. The peak incidence of HFMD typically fell between May and July, exhibiting pronounced seasonality. The emergence of COVID-19 pandemic changed the ratio of severe illness. In addition, the best-fitted seasonal ARIMA model was identified as (2,0,2)(1,0,1)12. The incidence of severe cases decreased significantly following the introduction of the vaccine to the market (χ2 = 109.9, p < 0.05). The number of hospitalized HFMD cases in Henan Province exhibited a seasonal and declining trend from 2014 to 2023. Non-pharmacological interventions implemented during the COVID-19 pandemic have led to a reduction in the incidence of severe illness.

Construction and evaluation of a predictive model for the types of sleep respiratory events in patients with OSA based on hypoxic parameters.

To explore the differences and associations of hypoxic parameters among distinct types of respiratory events in patients with obstructive sleep apnea (OSA) and to construct prediction models for the types of respiratory events based on hypoxic parameters. A retrospective analysis was conducted on a cohort of 67 patients with polysomnography (PSG). All overnight recorded respiratory events with pulse oxygen saturation (SpO2) desaturation were categorized into four categories: hypopnea (Hyp, 3409 events), obstructive apnea (OA, 5561 events), central apnea (CA, 1110 events) and mixed apnea (MA, 1372 events). All event recordings were exported separately from the PSG software as comma-separated variable (.csv) files, which were imported into custom-built MATLAB software for analysis. Based on 13 hypoxic parameters, artificial neural network (ANN) and binary logistic regression (BLR) were separately used for construction of Hyp, OA, CA and MA models. Receiver operating characteristic (ROC) curves were employed to compare the various predictive indicators of the two models for different respiratory event types, respectively. Both ANN and BLR models suggested that 13 hypoxic parameters significantly influenced the classification of respiratory event types; The area under the ROC curves of the ANN models surpassed those of traditional BLR models respiratory event types. The ANN models constructed based on the 13 hypoxic parameters exhibited superior predictive capabilities for distinct types of respiratory events, providing a feasible new tool for automatic identification of respiratory event types using sleep SpO2.

Robust estimate for count time series using GLARMA models: An application to environmental and epidemiological data

The Generalized Linear Autoregressive Moving Average (GLARMA) model has been used in epidemiological studies to evaluate the impact of air pollutants on health. Due to the nature of the data, a robust approach for the GLARMA model is proposed here based on the robustification of the quasi-likelihood function. Outlying observations are bounded separately by weight functions on covariates and the Huber loss function on the response variable. Some technical issues related to the robust approach are discussed and a Monte Carlo study revealed that the robust approach is more reliable than the classic one for contaminated data with additive outliers. The real data analysis investigates the impact of PM10 in the number of deaths by respiratory diseases in Vitória, Brazil.

Predictive Modelling of Network Capacity Demands: A Machine Learning Approach for Global Enterprise Backbone Networks

In the dynamic landscape of global enterprise networks, accurate capacity forecasting is paramount for ensuring optimal resource allocation and preventing service disruptions. This paper presents a hybrid machine learning methodology that combines Autoregressive Integrated Moving Average (ARIMA) models with additional techniques to enhance the accuracy and reliability of network capacity forecasts. By leveraging historical traffic data and incorporating external factors, we develop a predictive model that outperforms traditional methods and adapts to the evolving demands of modern networks. The effectiveness of our approach is validated through rigorous testing against established benchmarks, demonstrating significant improvements in forecasting accuracy.

Forecasting of Inland Fish Production: Case of Catla (&lt;em&gt;Catla catla&lt;/em&gt;) and Gift Tilapia (&lt;em&gt;Oreochromis niloticus&lt;/em&gt;) Production in Udawalawa Reservoir in Sabaragamuwa Province, Sri Lanka

This study focused on assessing and forecasting Catla (Catla catla) and Tilapia (Oreochromis niloticus) production in the Udawalawe reservoir in Sri Lanka using an 11-year time series data from 2010 to 2021. The relevant secondary data were collected from the National Aquaculture Development Authority of Sri Lanka (NAQDA). Unit root tests, Dickey-Fuller tests, and Seasonal Auto Regressive Integrated Moving Average (SARIMA) models and trend analysis were applied for the data analyses. According to the results, stationarity behaviour was observed in both Catla and Tilapia production information series. Hence, SARIMA (2,2,1) (1,0,1)12 is the appropriate empirical model for forecasting Catla production. Furthermore, the maximum average landings of Catla production were observed in the March-June time period. SARIMA (1,0,1) (1,1,1)12 was indicated as the most suitable model to forecast Tilapia production in the reservoir. The maximum average of Tilapia production was also observed in the March-June period. Both the SARIMA model and trend analysis gave approximately equal production information regarding Catla and Tilapia. Thus, these findings showed that the SARIMA model has more accuracy than trend analysis when analysing the time series information for short durations. However, when the duration becomes larger, forecasting is not suitable using the SARIMA model, and trend analysis gives a clear view up to a certain level. Both Catla and Tilapia production exhibited significant changing patterns and multiple seasonalities. Thus, trend analysis gives valuable insights than SARIMA model in long term predictions in case of this study. These findings are highly important for enhancing fish production in this reservoir, as they predict future fish catches under the current reservoir conditions.

Modelling Petrol Prices in Kenya from 2014 to 2023 Using Sarimax Model: A Case Study of Nairobi County

The requirement for petrol price information is crucial for majority of enterprises. This is because fluctuations in petrol prices impact inflation hence affecting daily lives of citizens. In analyzing the prices of petrol, researchers have employed several models but encountered various limitations. These limitations include; the Error Correction Model can examine only one co-integrating association. The Vector Autoregression (VAR) model does not account for the structural changes in the data. Additionally, the AutoRegressive Integrated Moving Average (ARIMA) model does not take into consideration the seasonal component in the data. The Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model assumes that over time the volatility is constant. Moreover, the Seasonal Autoregressive Integrated Moving Average (SARIMA) model does not integrate the external factors. Hence in this study Seasonal Autoregressive Integrated Moving Average with Exogenous Variables (SARIMAX) model was employed since it captures seasonality in data and incorporates the exogenous variables. The research’s aim was to model prices of petrol in Kenya for the period between 2014 to 2023 with exchange rates as an external factor. Secondary data was obtained from Energy and Petroleum Regulatory Authority (EPRA), Kenya National Bureau of Statistics (KNBS) and Central Bank of Kenya (CBK) websites. R software was used to analyze the data. By the use of historical data of petrol prices and exchange rates, the study sought to fit the best Seasonal Autoregressive Integrated Moving Average with Exogenous Variables (SARIMAX) model, validate the model and predict the petrol prices. The petrol price data was found to be non-stationary using Augmented Dickey Fuller test (ADF). Regular differencing was conducted to make the data stationary. Seasonal differencing due to seasonality component available in the data was also performed. Best SARIMAX model was chosen from various SARIMAX models according to Box-Jenkins methodology which uses least Akaike Information Criterion (AIC) value. SARIMAX (0,1,1)(2,1,2)&amp;lt;sub&amp;gt;12&amp;lt;/sub&amp;gt; model was selected since it had least Akaike Information Criterion (AIC) value of 656.3733 and the model validated using the hold out technique. The forecasts errors from the training set were; Mean Squared Error (MSE)=10.4970, Root Mean Square Error (RMSE)=3.239911, Mean Absolute Percentage Error (MAPE)=2.309268% while those from the testing set were; Mean Squared Error (MSE)=3271.1012, Root Mean Square Error (RMSE)=57.193542, Mean Absolute Percentage Error (MAPE)=26.695390%. There was less error in the training set than in the testing set as it was expected hence the model suited the data well and could be used for future predictions. The model was then used for five year forecast into the future. This study’s findings will offer sound suggestions to policymakers, businesses and consumers. This study recommends a model to be fitted using other factors affecting petrol prices and fitting Fourier terms, Behavioral Assessment Tools (BATS) and Trigonometric Box-Cox ARMA Trend Seasonal (TBATS) models.

Longer-term impacts of the COVID-19 pandemic on obstructive sleep apnoea (OSA)-related healthcare: a province-based study

RationaleFollowing marked reductions in sleep medicine care early in the COVID-19 pandemic, there is limited information about the recovery of these services. We explored long-term trends in obstructive sleep apnoea...

Advancing Cardiovascular Mortality Trend Analysis: A Machine Learning Approach to Predict Future Health Policy Needs.

This study investigates the forecasting of cardiovascular mortality trends in Greece's elderly population. Utilizing mortality data from 2001 to 2020, we employ two forecasting models: the Autoregressive Integrated Moving Average (ARIMA) and Facebook's Prophet model. Our study evaluates the efficacy of these models in predicting cardiovascular mortality trends over 2020-2030. The ARIMA model showcased predictive accuracy for the general and male population within the 65-79 age group, whereas the Prophet model provided better forecasts for females in the same age bracket. Our findings emphasize the need for adaptive forecasting tools that accommodate demographic-specific characteristics and highlight the role of advanced statistical methods in health policy planning.

Stock price prediction using LSTM neural networks: Techniques and applications

The prediction of stock prices has garnered significant attention due to the potential financial gains and complex questions involved. This paper elaborates a comparison between the Long Short-Term Memory (LSTM) model, optimised using the early-stopping method, and the conventional mathematical method Autoregressive Integrated Moving Average Model(ARIMA), which is conducted using the S&amp;P500 from 2022, May 01 to 2024, May 01. The results indicate that the LSTM surpasses ARIMA. To be more specific, LSTM achieves a 92% reduction in error rates compared to ARIMA. In addition, when the optimised LSTM is implemented in 6 different stocks, the results indicate a negative correlation between the volatility and accuracy of the stocks. This study demonstrates the advantages of optimised LSTM for predicting stock prices, and the importance of market volatility as a crucial aspect that significantly impacts the accuracy of stock price forecast.

Fusion of Hierarchical Optimization Models for Accurate Power Load Prediction

Accurate power load forecasting is critical to achieving the sustainability of energy management systems. However, conventional prediction methods suffer from low precision and stability because of crude modules for predicting short-term and medium-term loads. To solve such a problem, a Combined Modeling Power Load-Forecasting (CMPLF) method is proposed in this work. The CMPLF comprises two modules to deal with short-term and medium-term load forecasting, respectively. Each module consists of four essential parts including initial forecasting, decomposition and denoising, nonlinear optimization, and evaluation. Especially, to break through bottlenecks in hierarchical model optimization, we effectively fuse the Nonlinear Autoregressive model with Exogenous Inputs (NARX) and Long-Short Term Memory (LSTM) networks into the Autoregressive Integrated Moving Average (ARIMA) model. The experiment results based on real-world datasets from Queensland and China mainland show that our CMPLF has significant performance superiority compared with the state-of-the-art (SOTA) methods. CMPLF achieves a goodness-of-fit value of 97.174% in short-term load prediction and 97.162% in medium-term prediction. Our approach will be of great significance in promoting the sustainable development of smart cities.

Information Design and Sharing in Supply Chains

We study the interplay between inventory replenishment policies and information sharing in the context of a two-tier supply chain with a single supplier and a single retailer serving an independent and identically distributed Gaussian market demand. We investigate how the retailer’s inventory policy impacts the supply chain’s cumulative expected long-term average inventory costs [Formula: see text] in two extreme information-sharing cases: (a) full information sharing and (b) no information sharing. To find the retailer’s inventory policy that minimizes [Formula: see text], we formulate an infinite-dimensional optimization problem whose decision variables are the MA([Formula: see text]) coefficients that characterize a stationary ordering policy. Under full information sharing, the optimization problem admits a simple solution and the optimal policy is given by an MA(1) process. On the other hand, to solve the optimization problem under no information sharing, we reformulate the optimization from its time domain formulation to an equivalent z-transform formulation in which the decision variables correspond to elements of the Hardy space H2. This alternative representation allows us to use a number of results from H2 theory to compute the optimal value of [Formula: see text] and characterize a sequence of ϵ-optimal inventory policies under some mild technical conditions. By comparing the optimal solution under full information sharing and no information sharing, we derive a number of important practical takeaways. For instance, we show that there is value in information sharing if and only if the retailer’s optimal policy under full information sharing is not invertible with respect to the sequence of demand shocks. Furthermore, we derive a fundamental mathematical identity that reveals the value of information sharing by exploiting the canonical Smirnov–Beurling inner–outer factorization of the retailer’s orders when viewed as an element of H2. We also show that the value of information sharing can grow unboundedly when the cumulative supply chain costs are dominated by the supplier’s inventory costs. Funding: R. Caldentey acknowledges the University of Chicago Booth School of Business for financial support. Supplemental Material: The online appendix is available at https://doi.org/10.1287/moor.2023.008 .

Determination and analysis of 4-hydroxyphenylacetamide solubility in different solvents by Hansen solubility parameters, solvent effect and thermodynamic properties

4-Hydroxyphenylacetamide (HPAD) is commonly used in the synthesis of high value-added chemicals in the fields of medicine, food and agriculture, such as atenolol, p-hydroxyphenylacetic acid, L-phenylalanine and D-phenylalanine. HPAD relies on chemical synthesis, involving multi-step reactions and multiple chemical catalysts. It is crucial to select appropriate organic solvent to improve yield and purity. In this work, the solubility of HPAD in 12 pure solvents (water, acetonitrile, methanol, ethanol, n-propanol, isopropanol, n-butanol, n-hexane, n-heptane, methyl acetate, ethyl acetate, n-butyl acetate) and 3 binary solvents (n-hexane + methanol, acetonitrile + methanol, n-heptane + methanol) was investigated in the range of 278.15–323.15 K. The solubility of HPAD in the selected solvents was positively correlated with the temperature. Hansen solubility parameters were used to study the dissolution behavior of HPAD. Then, the solvent effect was analyzed by KAT-LSER model, and it was found that the hydrogen bond acidity and nonspecific dipolarity/polarizability of solvents favored the dissolution of HPAD. Moreover, the modified Apelblat model (MA model), λh model, CNIBS/R-K model (CNK model), Jouyban-Acree model (JA model), and SUN model were used to fit and analyze experimental data. The results showed that the MA model and the CNK model were the best models for single and binary solvents, respectively. Finally, van’t Hoff analysis showed that the dissolution of HPAD in the selected solvents was an endothermic and entropic driven process. This work provided reference data for the separation, purification and crystallization of HPAD, and had certain guiding significance for its industrial production and application.

Comparative Analysis of ARIMA and LSTM for Forecasting Maximum Wind Speed in Kupang City, East Nusa Tenggara

This study compares the Autoregressive Integrated Moving Average (ARIMA) and Long Short-Term Memory (LSTM) models for predicting maximum wind speed based on accuracy measured by Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE). Based on the results of the research, the LSTM model is better than the ARIMA model in predicting maximum wind speed in Kupang City, East Nusa Tenggara Province. The best LSTM model has hyperparameters of 200 epochs; batch size of 32; learning rate of 0,001; and 8 neurons. Based on the evaluation results of predicted data against actual data, the MAPE value of the LSTM model is 19,40%. The benefit of this research is that it can contribute to the literature on the development of wind utilization as a basis for building power plants on small islands as a renewable resource, particularly in Kupang City, East Nusa Tenggara.

Time series modeling of coastal fishery landings on the Southwestern Atlantic shelf: Influence of environmental drivers

AbstractTime‐series modeling of fisheries provides insights into stock tendencies and enables short‐term forecasting of landings, aiding decision makers in establishing management priorities. The Rio de la Plata Estuary and its maritime front sustain valuable fisheries for Argentina and Uruguay, with striped weakfish (Cynoscion guatucupa), whitemouth croaker (Micropogonias furnieri), and Argentine hake (Merluccius hubbsi) historically representing highest catches. However, their landings have declined in recent decades. To support resource management, we investigated the effectiveness of Autoregressive Integrated Moving Average (ARIMA) models in capturing fishery landing dynamics and providing reliable short‐term predictions. The best models exhibited a good fit and accurately captured the overall trends of landings. Residual variability unaccounted for by the model was analyzed in relation to time‐lagged environmental conditions. A wavelet coherence analysis was employed to examine the effect of the most significant variables on landings. Results revealed that environmental conditions affect differentially landings of each species as a result of their particular ecological traits. Turbidity and salinity affected mainly M. furnieri, which inhabits the innermost part of the estuary. Additionally, C. guatucupa, inhabiting the outer estuary and coastal region, exhibited a stronger association with river runoff compared to M. hubbsi, which inhabits the continental shelf. This study provides the first evidence of ARIMA models' reliability in representing the temporal evolution of catch in these fisheries, offering valuable tools for short‐term landings forecasting and facilitating sustainable management. Wavelet analysis findings will also contribute to enhancing our comprehension of trends in the correlation between environmental conditions and commercial landings.

Predicting the epidemiological trend of acute hemorrhagic conjunctivitis in China using Bayesian structural time-series model

This study aims to explore the application value of the Bayesian Time Structure Sequence (BSTS) model in estimating the acute hemorrhagic conjunctivitis (AHC) epidemics. The reported AHC cases spanning from January 2011 to October 2022 in China were collated. Utilizing R software, the BSTS and Autoregressive Integrated Moving Average (ARIMA) models were constructed using the data from January 2011 to December 2021. The prediction effect of both models was compared using the data from January to October 2022, and finally the AHC incidence from November 2022 to December 2023 was predicted. The results indicated that forecast errors under the BSTS model were lower than those under the ARIMA model. The actual AHC incidence in July 2022 from the ARIMA model deviated from the 95% confidence interval (CI) of the predicted value. However, the observed AHC incidence from the BSTS model fell within the 95% CI of the predicted value. Notably, the BSTS model predicted 26,474 new AHC cases in China from November 2022 to December 2023, exhibiting better prediction performance compared to the ARIMA model. This indicates that the BSTS model possesses a high application value for forecasting the epidemic trends of AHC, making it a valuable tool for disease surveillance and prevention strategies.

EEMD-ARIMA Model for annual precipitation forecasting to aid weather insurance decision-making research

Climate risk poses significant threats to human life and property. Climate insurance can effectively mitigate and disperse these risks. This paper addresses the weakness in weather risk prediction for climate insurance formulation by combining Ensemble Empirical Mode Decomposition (EEMD) and Autoregressive Integrated Moving Average (ARIMA) models. Four models, namely EMD, EEMD, ARIMA, and EMD-ARIMA, were established for modeling and forecasting Chinas annual precipitation data. The results show that the EEMD-ARIMA model can suppress the modal aliasing problem in time series and has the best fit compared to other models. This model can more accurately describe the variation in annual precipitation in forecasting applications, providing significant predictive value for insurance companies and government decisions regarding insurance and climate risk management.