Autoregressive Moving Average Research Articles

Application of feature fusion strategy for monitoring the condition of nitrogen filled tires using tree family of classifiers

The tire pressure monitoring systems (TPMS) are dedicated vehicle systems that calculate the tire pressure under various conditions. Proper maintenance of tire pressure can have a significant impact on enhancing vehicle handling, vehicle performance, occupant safety, comfort and fuel efficiency. Recent years have seen a shift in the preference for nitrogen-filled tires over air filled due to the superior thermal stability and uniform pressure management characteristics displayed by nitrogen-filled tires. The present article details the application of machine learning in TPMS to provide more insights into the tire behaviour for four different tire conditions (puncture, idle, high and normal). This paper specifically focuses on the collaborative approach that combines various features extracted with tree-based classifiers. Vertical wheel hub vibrations were captured using an accelerometer from which distinct features like autoregressive moving average (ARMA), statistical and histogram were extracted. With the application of J48, the most significant and contributing features were identified for every feature set extracted that was fed into tree-based classifiers. The best-performing classifier for every feature set was determined to be 95.83% (statistical–random forest), 93.75% (histogram-optimized forest) and 93.75% (ARMA–random forest). Furthermore, an extensive analysis was carried out to determine the impact of the feature fusion approach on feature combinations like statistical-histogram, histogram-ARMA, statistical-ARMA and statistical-histogram-ARMA. The experimental results indicate a commendable classification accuracy of 97.92% for a feature fusion of statistical-histogram-ARMA features with a forest penalizing attributes algorithm.

Estimation of wiener nonlinear systems with measurement noises utilizing correlation analysis and Kalman filter

AbstractThis paper is concerned with parameter estimation of Wiener systems with measurement noises employing correlation analysis method and adaptive Kalman filter. The presented Wiener system consists of two series blocks, that is, a dynamic block represented by auto‐regressive moving average (ARMA) model, and static nonlinear block established by neural fuzzy model. Aim at estimating separately the two blocks, the separable signals are introduced. First, applying the separable signals to decouple the identification of linear dynamic block from that of static nonlinear block, then ARMA model parameters are estimated employing correlation function‐based least squares principle. Moreover, aiming at handle with error caused by colored measurement noise, adaptive Kalman filter technique and cluster method are introduced to estimate parameter of the nonlinear block and noises model, enhancing parameter estimation precision. The accuracy and applicability of estimated scheme presented are verified through numerical simulation and nonlinear process, the results demonstrate that it is feasible for estimating the Wiener systems in the presence of colored measurement noises.

Transformed‐Linear Models for Time Series Extremes

To capture the dependence in the upper tail of a time series, we develop non‐negative regularly varying time series models that are constructed similarly to classical non‐extreme ARMA models. Rather than fully characterizing tail dependence of the time series, we define the concept of weak tail stationarity which allows us to describe a regularly varying time series via a measure of pairwise extremal dependencies, the tail pairwise dependence function (TPDF). We state consistency requirements among the finite‐dimensional collections of the elements of a regularly varying time series and show that the TPDF's value does not depend on the dimension of the random vector being considered. So that our models take non‐negative values, we use transformed‐linear operations. We show existence and stationarity of these models, and develop their properties such as the model TPDFs. We fit models to hourly windspeed and daily fire weather index data, and we find that the fitted transformed‐linear models produce better estimates of upper tail quantities than a traditional ARMA model, classical linear regularly varying models, a max‐ARMA model, and a Markov model.

Hemispheric Distribution of Halo Coronal Mass Ejection Source Locations

The hemispheric asymmetry of solar activity is one of the essential physical consequences of the interior dynamo process. However, the hemispheric distribution of halo coronal mass ejection (HCME) source locations has not been investigated in detail. Based on the HCME catalog identified from the Large Angle and Spectrometric Coronagraph Experiment on board the Solar and Heliospheric Observatory, we perform a hemispheric distribution analysis of the HCME source locations from 1996 April to 2022 June. The main results are as follows. (1) The HCME source locations are confined to the active region belt, and there is no “rush to the poles” phenomenon that is unique to large-scale magnetic activity. (2) The HCME source locations exhibit a general hemispheric asymmetry, and autoregressive moving-average model results show that the asymmetry of HCME source locations is significantly different from that of sunspot activity. (3) The hemispheric distribution of cycle 24 is different from that of cycle 23, potentially as a result of the heliospheric dynamic pressure having noticeably decreased after the polarity reversal of cycle 23. Our results contribute to a more comprehensive understanding of the hemispheric asymmetry of energetic magnetic structures and give a new perspective on understanding the geoeffectiveness of HCMEs.

Estimation of functional ARMA models

Estimation of functional ARMA models

Research on Vegetable Pricing and Replenishment Strategies Based on ARMA Models

This paper explores the importance of pricing and replenishment issues of vegetable items for merchant operations. First, data related to vegetable stocking were systematically collected to assess the correlation between different categories. Through Q-Q plot analysis, it was determined that the data did not conform to a normal distribution, so the Spearman correlation coefficient was chosen for further quantitative analysis. Considering the large number of individual vegetable categories, in order to deal with the correlation between different individual items, a hierarchical clustering method was introduced in this paper, which classified them into three categories: low volume, medium volume and high volume. In order to predict the future vegetable intake, this study adopted the seasonal ARMA model and applied the Pearson's correlation coefficient for correlation analysis by combining the cost utilization rate as well as the total sales volume. In addition, the relationship between sales and cost margin was fitted with the help of support vector machine. Finally, this study constructs a single optimization model with the objective of maximizing the revenue of the superstore. The innovation of this paper is the combination of various statistical and machine learning methods in order to solve the pricing and replenishment problem of vegetable items, aiming to improve the efficiency of merchant operations.

Prediction and Classification Models of Wordle Data Set Based On ARMA, XGBoost

This study intends to use machine learning method to solve the problem of predicting the number of word guessing and the difficulty of word guessing in natural language. Firstly, this study smoothed the data according to the COF method. According to the images of the sample autocorrelation function and the sample partial autocorrelation function, the ARMA model is constructed and the coefficient of the AMRA model is calculated, and the number of word contest people on March 1, 2023 is predicted to drop to [18002,20622]. Second, based on the seven attributes of the word, the XGBoost classification model is used to predict the value of the first attempt, and then the XGBoost prediction model is used to predict the results of the other attempts. Eerie distribution of different attempts to {0,8.890, 17.650, 28.191, 32.451, 11.349, 1.483}. The average accuracy of the model on the test set is 79.4%.

Predictive Analysis Based on Prophet Model: Evidence from the Number of Epidemic Infections

This study makes a prediction of infection numbers in the future and investigates the correlation between the new cases and the stock price of companies in three different fields. In the first part of the prediction, we use three methods to make predictions of the future infected number, including the ETS, Auto-Regressive Moving Average (ARIMA), and Prophet model. In the next part, we use the GARCH model to discuss the correlation between infection numbers and stock prices. Considering the heterogeneity, we choose three different companies’ stocks to represent three industries respectively. They are Apple, Amazon and Pfizer. The result indicates that the stock price of Apple is negatively correlated to the infection number, the relationship between the infection number and the price of Amazon is inconspicuous, while the price of Pfizer is positively correlated with the development of the pandemic. Thus, we draw the conclusion that the impact of the epidemic is strong to manufacturing, is not obvious to the Internet industry, and boosts the development of the medical corporation.

ANALYSIS OF THE EFFECT OF EXCHANGE RATE FLUCTUATIONS ON BITCOIN RETURNS: A TIME SERIES APPROACH IN A DIGITAL FINANCE PERSPECTIVE

Cryptocurrencies, including Bitcoin, have been the subject of great attention in the media and academia due to their large price fluctuations. This study aims to analyze the effect of exchange rate fluctuations (USD/AUD, USD/EUR, USD/GBP, and USD/JPY) on Bitcoin returns in the period 2014 to 2019 using a time series approach. We applied the ARMA model and the Maximum Likelihood Estimation method to analyze the data. The results showed that exchange rate fluctuations had no significant effect on Bitcoin's returns when confidence was measured at a rate of 95 percent. However, when significance was measured at a rate of 90 percent, GBP was found to have a significant effect on Bitcoin returns. This may be due to common factors affecting the simultaneous returns of USD/GBP and Bitcoin in recent times. Future studies may consider investigating the effect of exchange rate fluctuations on other cryptocurrencies, other than Bitcoin, to gain a more comprehensive understanding of the effect of exchange rates on digital assets in a digital finance perspective.

Time series analysis and development of simulation model for monthly rainfall using ARIMA model

Rainfall holds critical significance for water resource applications, particularly in rainfed agricultural systems. This study employs the Autoregre ssive Integrated Moving Average (ARIMA) technique, a data mining approach commonly used for time series analysis and future forecasting. Given the increasing importance of climate change forecasting in averting unexpected natural hazards such as floods, frost, forest fires, and droughts, accurate weather data forecasting becomes imperative. The objective of this study was to develop a Seasonal Auto-Regressive Integrative Moving Average (SARIMA) model for forecasting monthly rainfall in Junagadh Station, Gujarat. Utilizing 50 years of historical data (1968 to 2016), the SARIMA model predicts weekly rainfall for the subsequent five years (2018 to 2022). Through comprehensive evaluation using ACF and PACF plots, AIC, SBC, MAPE, and MAE values, the study identifies SARIMA (1,0,0)(3,1,1)12 as the optimal model, offering the most accurate prediction. The robust results affirm that the SARIMA model provides reliable and satisfactory weekly rainfall predictions. This research contributes valuable insights into the precision and efficacy of SARIMA models for rainfall forecasting, aiding in strategic water resource management in the Junagadh region.

Identification of a different design of a photovoltaic thermal collector based on fuzzy logic control and the ARMAX model

This study investigates the effects of absorber design on the performance of a water-based photovoltaic thermal collector (PVT). In this regard, three different absorber designs with the same pipe diameter and length were tested and compared experimentally in Cairo, Egypt, during summer 2021. Furthermore, artificial intelligence techniques were used to create a model to identify the PVT system and define the input–output relationship in state space representation. This is to get a more thorough understanding and deeper evaluation of the PVT performance with various absorber designs to achieve the best possible system performance under varied operating situations and weather. The fuzzy logic control is examined by Autoregression Moving Average using an external input model (ARMAX), which relies on the autoregression moving average to achieve system performance. According to the study's findings, at a maximum water flow rate of 3 L/min, the proposed PVT system can convert solar energy with an overall efficiency of 69.6 %, 64.6 %, and 63.5 %, respectively, with a pressure loss of about 29.5 kPa, 29.33 kPa, and 24.79 kPa for absorbers A, B, and C. Furthermore, the challenging suggested fuzzy control system for the PVT system provides knowledge to comprehend the system complexity since the connection is formed by connecting 5 inputs with 3 outputs through its representation in the state space representation.

Identification of Multi-Innovation Stochastic Gradients with Maximum Likelihood Algorithm Based on Ship Maneuverability and Wave Peak Models

This paper investigates the problem of real-time parameter identification for ship maneuvering parameters and wave peak frequency in an ocean environment. Based on the idea of Euler discretion, a combined model of ship maneuvering and wave peak frequency (ship–wave) is made a discretion, and a discrete-time auto-regressive moving-average model with exogenous input (ARMAX) is derived for parameter identification. Based on the ideas of stochastic gradient identification and multi-innovation theory, a multi-innovation stochastic gradient (MI-SG) algorithm is derived for parameter identification of the ship–wave discretion model. Maximum likelihood theory is introduced to propose a maximum likelihood-based multi-innovation stochastic gradient (ML-MI-SG) algorithm. Compared to the MI-SG algorithm, the ML-MI-SG algorithm shows improvements in both parameter identification accuracy and identification convergence speed. Simulation results verify the effectiveness of the proposed algorithm.

Demand Forecasting Analysis of Body Scrub Product at PT. XYZ Using Autoregressive Integrated Moving Average (ARIMA) Method

PT. XYZ is one of the private companies engaged in the cosmetics industry, especially as a manufacturer of SPA products with natural raw materials. The SPA products produced by PT. XYZ are various kinds of scrubs, body scrubs, masks, essential oils, soaps and other products. Body scrub is the most popular product or best seller in this company. The purpose of this study is to find the best ARIMA model in calculating demand forecasting of body scrub products at PT. XYZ for the period January to December 2024. The method used in this research is the Autoregressive Moving Average (ARIMA) method. There are two ARIMA models that can be used in this study, namely ARIMA (1,1,0) and ARIMA (0,1,1). The selected ARIMA model used to calculate body scrub forecasting at PT. XYZ is the ARIMA (1,1,0) model because it has a smaller error value, namely MSE = 745.46 and MAPE = 7.40%. The results of forecasting the demand for body scrub products at PT. XYZ for the period January to December 2024 using the ARIMA (1,1,0) model show that demand has increased every month with an average demand of 343.4 kg. The error obtained by comparing the forecasting results with the actual sales data for 6 months, namely January - June 2024 is 2.45%, which means that the accuracy of forecasting in 6 months reaches 97.55%.

Predictive Analysis of Maritime Congestion Using Dynamic Big Data and Multiscale Feature Analysis

The maritime industry is one of the most crucial sectors in the global economy, facilitating the transportation of goods and commodities across vast distances. However, maritime network congestion has become an increasingly critical challenge that significantly affects shipping efficiency and the overall operational performance of the industry. This study proposes an innovative congestion prediction approach using dynamic big data analysis of vessel trajectories and multiscale feature analysis. First, the dynamic analysis of vessel trajectories aims to extract valuable information from ships’ data as they navigate the oceans, enabling proactive traffic management and optimized routing. Second, the multiscale feature analysis provides a comprehensive understanding of maritime network congestion by examining it from different perspectives and scales, leading to more accurate predictions and effective congestion management strategies. Furthermore, this study introduces an enhanced Faster R‐CNN vessel detection model for real‐time tracking, integrating convolutional and SKNet networks. To improve short‐term traffic flow prediction accuracy, this study employs multiscale feature analysis through wavelet transformation. The foundational traffic data undergo wavelet decomposition for a detailed representation across frequencies. Gated recurrent unit (GRU) neural network and autoregressive moving average (ARMA) models are utilized to predict trend and noise components, respectively. Fusion of predictions demonstrates superior accuracy and is validated against real data. Empirical results showcase minimal errors and heightened prediction accuracy compared to actual data.

Analysis of the Impact of Billing, Authorizations and Unknown Debts on the Technical Reserve of Health Promotion Companies: Case of HPC in Colombia

The management of the technical reserve in Empresas Promotoras de Salud (EPS) in Colombia demonstrates adequate financial capacity to ensure the effective delivery of healthcare services. Nevertheless, the reserve's inherent nature and the unpredictability it engenders in planning engender financial risks that impair the long-term viability of such companies. The study aims to examine how filed invoices, unsubmitted authorizations, and estimated unknown debts affect the technical reserve of EPSs in Colombia from 2016 to 2021 using statistical methods like Online Linguistic Support, Autoregressive Moving Average, Autoregressive Conditional Heteroscedasticity, and Vector Autoregression. A descriptive and correlational study was conducted, considering current regulatory policies and the health insurance industry. Time series data were analyzed and revealed an inverse correlation between the explanatory and response variables. The method permits the assessment of two variables that impact technical reserve management: an increase in filed invoices and undetected claims results in decreased reserves, and filed authorizations have the opposite effect. Technical term abbreviations are explained upon their initial use in the text. Citations follow a consistent footnote style and formatting features. Similarly, we forecasted the impulse response function for the effect of technical reserves and suggested developing a predictive methodology to manage, regulate, or eradicate financial management vulnerabilities linked to HPC in Colombia. We emphasize the significance of precise authorizations for medical services since undiscovered debts may lead to financial losses for health care providers. A systematic approach is necessary to adhere to applicable regulations, including Law 100 of 1993 and associated resolutions and decrees. Healthcare providers can enhance their financial performance by ensuring accurate authorization and billing practices, thereby avoiding financial penalties.

Using artificial intelligence techniques and econometrics model for crypto-price prediction

In today's financial landscape, individuals face challenges when it comes to determining the most effective investment strategies. Cryptocurrencies have emerged as a recent and enticing option for investment. This paper focuses on forecasting the price of Ethereum using two distinct methods: artificial intelligence (AI)-based methods like Genetic Algorithms (GA), and econometric models such as regression analysis and time series models. The study incorporates economic indicators such as Crude Oil Prices and the Federal Funds Effective Rate, as well as global indices like the Dow Jones Industrial Average and Standard and Poor's 500, as input variables for prediction. To achieve accurate predictions for Ethereum's price one day ahead, we develop a hybrid algorithm combining Genetic Algorithms (GA) and Artificial Neural Networks (ANN). Furthermore, regression analysis serves as an additional prediction tool. Additionally, we employ the Autoregressive Moving Average (ARMA) model to assess the relationships between variables (dependent and independent variables). To evaluate the performance of our chosen methods, we utilize daily historical data encompassing economic and global indices from the beginning of 2019 until the end of 2021. The results demonstrate the superiority of AI-based approaches over econometric methods in terms of predictability, as evidenced by lower loss functions and increased accuracy. Moreover, our findings suggest that the AI approach enhances computational speed while maintaining accuracy and minimizing errors.

Fresh Vegetable Sales and Pricing Forecasting Based on Systematic Clustering and ARMA Modeling

Fresh Vegetable Sales and Pricing Forecasting Based on Systematic Clustering and ARMA Modeling

Using ARMA Models in Stochastic Enterprise Valuation

This article presents a method for estimating the variance of the firm or enterprise value distribution by incorporating temporal dependencies in cash flows using ARMA models. The analysis highlights the importance of considering these dependencies, as neglecting them can lead to a significant increase in variance and subsequent erroneous decision-making. By utilizing ARMA models, decision-makers can obtain a more accurate assessment of the underlying risks and make informed investment decisions based on a comprehensive understanding of the firm's value distribution. The proposed method provides valuable insights for evaluating the uncertainty associated with future cash flows and enhances the accuracy of investment decision processes.

Study on the Development Trends and Strategies of International Economy and Trade under the New Situation

Abstract In today’s increasingly globalized world, trade between countries has become more interconnected, and international trade has emerged as the primary driver of economic development for all nations. This paper proposes a standard trade model for the new situation using the Dixit-Stiglitz model. Starting from the consumer’s point of view, the two-stage maximum method is adopted. Using the Lagrange function, the demand utility function of consumer preferences is processed, and the model optimization scheme is deduced. The ARMA model is used to predict the volume of merchandise foreign trade, and the prediction range includes autoregressive and moving average processes. The indicators are used to evaluate the smoothness of the series within the sample period. A suitable sample of international trade data is selected. The results of the analysis are as follows: in the logistic autoregressive analysis test, the significance of all factors, except for the language and religion factors, passes the significance test of 0.001, and at the same time, the model goodness-of-fit is greater than 0.1, ranging from 0.115 to 0.175. In the analysis of the spatial evolution pattern of international economic trade and trade dynamic development, the spatial Gini coefficients of the first category of goods in 2000, 2014, and 2019 and the third category in 2000 are 0.4306, 0.4717, 0.4956, and 0.4673, respectively, which are lower than 0.5, showing a more balanced evolution pattern.

Application of a Log Likelihood Object of an ARMA(2,2) Model in Three Cryptocurrencies Such as Bitcoin, Shiba and Ripple

Application of a Log Likelihood Object of an ARMA(2,2) Model in Three Cryptocurrencies Such as Bitcoin, Shiba and Ripple