Autoregressive Moving Average Research Articles

Multi-Objective Optimization of Cell Voltage Based on a Comprehensive Index Evaluation Model in the Aluminum Electrolysis Process

In the abnormal situation of an aluminum electrolysis cell, the setting of cell voltage is mainly based on manual experience. To obtain a smaller cell voltage and optimize the operating parameters, a multi-objective optimization method for cell voltage based on a comprehensive index evaluation model is proposed. Firstly, a comprehensive judgment model of the cell state based on the energy balance, material balance, and stability of the aluminum electrolysis process is established. Secondly, a fuzzy neural network (FNN) based on the autoregressive moving average (ARMA) model is designed to establish the cell-state prediction model in order to finish the real-time monitoring of the process. Thirdly, the optimization goal of the process is summarized as having been met when the difference between the average cell voltage and the target value reaches the minimum, and the condition of the cell is excellent. And then, the optimization setting model of cell voltage is established under the constraints of the production and operation requirements. Finally, a multi-objective antlion optimization algorithm (MOALO) is used to solve the above model and find a group of optimized values of the electrolysis cell, which is used to realize the optimization control of the cell state. By using actual production data, the above method is validated to be effective. Moreover, optimized operating parameters are used to verify the prediction model of cell voltage, and the cell state is just excellent. The method is also applied to realize the optimization control of the process. It is of guiding significance for stabilizing the electrolytic aluminum production and achieving energy saving and consumption reduction.

Automotive Kit Demand Forecasting Using Advanced Forecasting Models: A Data-Driven Approach for Optimal Demand Forecasting

This study addresses the major challenges of forecasting automotive kit items(parts of vehicles) by enhancing the delivery of the products and managing the inventory. The kit items vary as per customers and it is unique on its own, where the uniqueness determines the vehicle parts. Customers are the major role players who provide the business hence, this study highlights various factors contributing to the customer’s choice of kit items with features consisting of vehicle name, original equipment manufacturer (OEM), Item Description (collection of vehicle parts) type of product (brand of vehicle) and monthly allotment of each kit item as per customer starting from 2021 April to 2024 January. We conducted an extensive analysis to assess a range of time series analysis techniques for predicting kit demand within the automotive industry, the methods we investigated encompassed Autoregressive (AR), Autoregressive Moving Average (ARMA) ,Autoregressive Integrated Moving Average (ARIMA), Seasonal Autoregressive Integrated Moving Average (SARIMA), Simple Exponential Smoothing (SES), Holt's Linear Trend Method - Double Exponential Smoothing, Triple Exponential Smoothing - Holt Winters, Long Short-Term Memory (LSTM) and advanced forecasting models such as prophet in evaluating the accuracy of these models, we employed key metrics such as Root Mean Squared Error (RMSE) and Mean Absolute Percentage Error (MAPE), this study aims to drive significant progress in the automotive industry by optimising inventory management reducing storage costs and improving delivery efficiency to ensure smooth business operations moreover the integration of visually engaging dashboards for real-time analysis of projected values plays a pivotal role in identifying crucial monthly demand trends this integration not only enhances operational efficiency but also fosters enriched customer engagement thereby facilitating sustained advancement within the automotive sector.

Effect of Structural Breaks on Stock Market Performance during COVID-19 Period in Sri Lanka

This study investigates the effects of structural breaks on the Colombo Stock Exchange (CSE) performance over the COVID-19 period. Stock market returns and volatility are used to proxy the stock market performance. Structural breaks were identified by using the Bai-Perron (2003) test. An ARMA (p,q) model fitted for stock returns was augmented using dummy variables for the structural breaks to measure the effect of structural breaks on stock market returns. The model was further extended as a volatility regression model (GARCH, EGARCH, or TGARCH) to measure the effect of structural breaks on stock market volatility. The results confirmed the presence of structural breaks following COVID-19-related news in CSE. Seventeen such breaks were identified. However, only three significantly influenced the stock market returns and the volatility. As a result, the study's consequences affect stockbrokers, multinational organizations, portfolio managers, and investors, allowing them to foresee market patterns and take preventative action in the event of structural breaks.

The Predictive Power of Monetary Policy on International Stock Market Returns—Evidence From TV-ARMAX Model

In this paper, we apply the time-varying ARMA model with exogenous variable (TV-ARMAX) to examine the predictive power of monetary policy on international stock returns. This method allows time-varying coefficient estimates and uses time-dependent cumulated variation penalty to filter noisy outlier data points. Based on a wide range of 31 countries, our method robustly outperforms other popular methods including the simple linear-regression model (SLM), the vector autoregression and its variants (VAR, TV-VAR, and VARX) and the ARMA model with exogenous variable (ARMAX).

Investigation of Radon, Total Electron Content and Linear and Nonlinear Variations of Meteorological Variables Due to Earthquakes: ARIMA and Monte Carlo Modelling

An Integrated Autoregressive Moving Average (ARIMA) - Monte Carlo Simulation (MCS) is proposed to analyze and model the anomalies of atmospheric and ground gases by an earthquake along the North Anatolian Fault Zone (Türkiye). Earthquakes, Soil radon gas and Total Electron Content (TEC) showed simultaneous anomalies. There are positive relationships between these three parameters. Also, positive relations between Rn, meteorology, and atmosphere are detected. The proposed ARIMA model and MCS for the Rn-TEC-Earthquake relationships of the measured data gave statistically significant results. This model and simulation showed statistically significant changes in the effects of microearthquakes, which are more difficult to detect than large earthquakes, especially on the ionospheric TEC.

Online learning of stable robust adaptive controllers design based on data-dependent feedback linearization with application to rotary inverted pendulum

AbstractThis study introduces an online (supervised) learning method to design nonlinear auto-regressive moving average (NARMA) controllers for feedback-linearized nonlinear single-input single-output (SISO) systems. The algorithm ensures Schur stability of the overall closed-loop system and provides adaptiveness and robustness for the NARMA controllers. The first stage of the method derives, in a data-dependent way, a feedback-linearized model of the nonlinear plant by using its input and output sample pairs. The method’s second stage, which constitutes the novel part of the presented study, builds up an online learning scheme for the linear auto-regressive moving average (ARMA) controller based on an already learned feedback-linearized model of the nonlinear plant. During online supervised learning, ARMA parameters of the feedback-linearized SISO plant model and the closed-loop ARMA model are computed by minimizing the plant identification and the closed-loop system tracking errors. Both errors are defined as $${{\ell}}_{1,{\varvec{\varepsilon}}}$$ ℓ 1 , ε , namely ε-insensitive loss functions that provide NARMA controller the robustness against noise and outliers. The proposed online learning control algorithm is applied to a rotary inverted pendulum model and to a real rotary inverted pendulum setup. The tracking performance of the developed controller is compared with those of the linear quadratic regulator and coupled sliding mode controller in terms of mean square error.

Multi-participant Decision-making Based on Digital Platforms: A Case Study for the Romanian Academy

The last decade culminating with the pandemic disrupted traditional business models in various industries and accelerated the digital transformation based on economy platforms. The main aim of this paper is to present the platform economy framework to facilitate peer-to-peer transactions for different business models and the basic aspects related to multi-participant/collaborative decision-making and evaluation of teleconference platforms and particularize the virtual meetings and conferences. The necessary steps for planning a teleconference and the results of the concept implementation have been extracted from Romanian Academy practical case study referring to multi-participant decision-making using teleconference platforms. A Seasonal Autoregressive Moving Average model (SARIMA) is employed to explain the number of participants in an online conference in the Romanian Academy between April 2020 to August 2023. This model has been used to make short-run dynamic and static forecasts on the horizon from September 2023 to December 2023.

Power consumption prediction for electric vehicle charging stations and forecasting income

Electric vehicles (EVs) are the future of the automobile industry, as they produce zero emissions and address environmental and health concerns caused by traditional fuel-poared vehicles. As more people shift towards EVs, the demand for power consumption forecasting is increasing to manage the charging stations effectively. Predicting power consumption can help optimize operations, prevent grid overloading, and power outages, and assist companies in estimating the number of charging stations required to meet demand. The paper uses three time series models to predict the electricity demand for charging stations, and the SARIMA (Seasonal Auto Regressive Integrated Moving Average) model outperforms the ARMA (Auto Regressive Moving Average) and ARIMA (Auto Regressive Integrated Moving Average) models, with the least RMSE (Root Mean Squared Error), MAE (Mean Absolute Error) and MAPE (Mean Absolute Percentage Error) scores in forecasting power demand and revenue. The data used for validation consists of charging activities over a four-year period from public charging outlets in Colorado, six months of charging data from ChargeMOD's public charging terminals in Kerala, India. Power usage is also forecasted based on wheels of vehicles, and finally, a plan subscription data from the same source is utilized to anticipate income, that helps companies develop pricing strategies to maximize profits while remaining competitive. Utility firms and charging networks may use accurate power consumption forecasts for a variety of purposes, such as power scheduling and determining the expected energy requirements for charging stations. Ultimately, precise power consumption forecasting can assist in the effective planning and design of EV charging infrastructure. The main aim of this study is to create a good time series model which can estimate the electric vehicle charging stations usage of power and verify if the firm has a good income along with some accuracy measures. The results show that SARIMA model plays a vital role in providing us with accurate information. According to the data and study here, four wheelers use more power than two and three wheelers. Also, DC charging facility uses more electricity than AC charging stations. These results can be used to determine the cost to operate the EVs and its subscriptions.

A hybrid model based on STL with simple exponential smoothing and ARMA for wind forecast in a Brazilian nuclear power plant site

In the event of a nuclear accident with release of radioactive effluents into the environment, the Environmental Control System (ECS) at the Central Nuclear Almirante Álvaro Alberto (CNAAA) nuclear complex estimates the movement of the radioactive plume produced and provides forecast for one and two hours ahead. The prediction is based on the current accident and atmospheric data collected from meteorological stations spread across the site. In this way, the ECS estimates the spatial dose and dose rates distribution at ground level. The one- and two-hour forecast made by the ECS use the naïve method, which repeats the last known value. Its results are satisfactory if the next value to be collected does not differ substantially from the last known value. However, observing real data, acquired in loco, this fact does not occur in practice, which can make the forecast considerably erroneous.The purpose of this work is to investigate new approaches to improve ECS forecasting method, making it more accurate in real situations. To this end, the Seasonal and Trend decomposition using Loess (STL) was used together with the Simple Exponential Smoothing (SES) method and the Autoregressive Moving Average (ARMA) model to forecast weather parameters at the Angra site. The STL method was chosen since it has a robust mathematical decomposition method, capable of satisfactorily dealing with time series that have a high frequency of acquisition, the so-called intraday series. This model uses the Loess method, which is a smoothing technique based on locally adjusted regressions. The SES method and ARMA model are the techniques that most adequately fit the trend and remainder components generated by the STL decomposition, respectively. In the scope of this work, the method is applied to forecast wind speed measurements made at one of the meteorological towers used by the ECS. Using the hybrid model developed, an increase the of 101.39% in the Mean Absolute Percentage Error (MAPE), was achieved when compared to the ECS forecast method.

Perspectives of Electricity Production from Biogas in the European Union

Biogas is a renewable energy source (RES). The aim of this research was to analyze the perspectives of electricity production from biogas in the European Union. The main source of information was data from Eurostat. We analyzed electricity production from biogas in the European Union (EU). The scope of this research was data from 2012 to 2021. First, we presented biogas production by feedstock type across the world. Then, we presented changes in electricity production from biogas in the EU. We used different methods to evaluate the changes in biogas production. First, we used the ARiMA (Autoregressive Moving Average) model to evaluate the stationarity of the time series. Our electricity production from biogas data proved to be stationary. Second, we elaborated on the prognosis of future changes in electricity production from biogas. The largest producer of biogas is the EU, and it is produced from crops, animal manure, and municipal solid waste. Our research found that the largest production from biogas in 2021 took place in Germany, Italy, and France. These countries have the greatest potential for electricity production from biogas, and they have spent significant funds on facilities and technology. Such countries as Ireland, Greece, Spain, France, Croatia, Poland, Portugal, Romania, Finland, and Sweden increased their electricity production from biogas in 2021 compared to 2020. According to our prognosis, the global production of biogas will increase from 62.300 TWh to 64.000 TWh in 2019–2026 (2.7% increase). In 2022–2026, such countries as Estonia (60.4%), Latvia (29.6%), Croatia (27.6%), Slovenia (10.9%), and Poland (8.2%) will increase their electricity production from biogas the most. In 2022–2026, such countries as Italy (0.68%), Portugal (1.1%), Greece (1.5%), Slovakia (2.3%), and Germany (2.6%) will increase their electricity production from biogas the least. Only Romania (−17.6%), Finland (−11.5%), Lithuania (−9.1%), and Malta (−1.06%) will decrease their production of electricity from biogas in 2022–2026. Such countries as Bulgaria (2344%), Denmark (590.9%), Croatia (449%), and France (183%) increased biogas consumption in 2013–2022. A decrease in the inland consumption of biogas in 2013–2022 was observed in Spain, Cyprus, Latvia, Luxembourg, Austria, and Slovenia.

Robust relation of streamwise velocity autocorrelation in atmospheric surface layers based on an autoregressive moving average model

We construct an autoregressive moving average (ARMA) model consisting of the history and random effects for the streamwise velocity fluctuation in boundary-layer turbulence. The distance to the wall and the boundary-layer thickness determine the time step and the order of the ARMA model, respectively. Based on the autocorrelation's analytical expression of the ARMA model, we obtain a global analytical expression for the second-order structure function, which asymptotically captures the inertial, dynamic and large-scale ranges. Specifically, the exponential autocorrelation of the ARMA model arises from the autoregressive coefficients and is modified to logarithmic behaviour by the moving-average coefficients. The asymptotic expressions enable us to determine model coefficients by existing parameters, such as the Kolmogorov and the Townsend–Perry constants. A consequent double-log expression for the characteristic length scale is derived and is justified by direct numerical simulation data with $Re_\tau \approx 5200$ and field-measured neutral atmospheric surface layer data with $Re_\tau \sim O(10^6)$ from the Qingtu Lake Observation Array site. This relation is robust because it applies to $Re_\tau$ from $O(10^4)$ to $O(10^6)$ , and even when the statistics of natural ASL deviate from those of canonical boundary-layer turbulence, e.g. in the case of imbalance in energy production and dissipation, and when the Townsend–Perry constant deviates from traditional values.

Generation and validation of comprehensive synthetic weather histories using auto-regressive moving-average models

As energy system design moves to more complex methods of optimisation including machine learning there is a significant need for more weather data than is available. One method to solve this is using synthetic data models such as the auto-regressive moving-average (ARMA) model which has been frequently utilised to create such data. This paper looks at extending the ARMA algorithm to generate solar components through the use of clearsky detrending, maintaining vector relationships and by leveraging physical relationships. The method for the creation of entirely synthetic weather data files including key weather variables for energy system analysis is presented. Furthermore, a detailed comparison of energy system simulations utilising both real and synthetic data is made using NREL’s System Advisor Model. Whilst good agreement is made for the solar variables, and other weather variables, ARMA methods often fail to capture the standard deviation and skew of annual weather distributions. Vector-ARMA is shown to maintain correlations between variables and thus generate data sets that perform similarly in energy system design. It is finally shown that the ARMA method fails to preserve day-today correlations in weather variables and thus over-predicts optimal energy storage by 21% for a residential solar application.

The Colouration in Dicrurus caerulescens (White-bellied Drongo) Follows the Precipitation and Temperature of the Environment

The variation in climatic conditions over space and time is considered a major driving force in speciation. Gloger's rule is one such rule that broadly explains the variation in the colouration of endotherms (birds and mammals) with the effects of climatic parameters. This predicts that endothermic animals tend to have darker colouration in warm and rainy climates. Here we have tried to explain the variation in the belly white colour in Dicrurus caerulescens (White-bellied Drongo), an overall black coloured bird with a variable white belly. The dark bellied birds are considered the subspecies D. c., and the pale bellied birds are considered D. c.insularis (in dryzone of Sri Lanka). A total number of 112 individuals of adult White-bellied Drongo, including field (n=23) and museum samples (n=36) from Sri Lanka (Total 59), and museum samples from India (n=45) and Nepal (n=8) which were deposited at the National Museum of Sri Lanka, Field Museum of Natural History, USA and the Natural History Museum, UK were taken into consideration. The length of the white belly was measured using a dial calliper adopting a standardized method to minimize inconsistencies. To understand how the extent of white belly varied with respect to precipitation and temperature, we adopted generalized least squares (GLS) methods accounting for spatial autocorrelation between data points. The climatic variables were extracted from the world climatic data through QGIS software. The GLS methods were carried out using the ―nlme‖ package in the R Software for different orders of Autoregressive-moving average (ARMA) for the squared value of white belly length. To find the best fitting model, we used the Akaike information criterion (AIC) using the package ―AICcmodavg‖ in the R. From this, we found a negative significant effect of the interaction between the temperature and precipitation for the white belly length over the studied area in ARMA (1, 1) correlation (-3.72e-01±-0.022, t= -2.23, p<0.05). The white belly is smaller; hence the birds are darker where there is higher temperature and precipitation. The reverse (pale belly) is true when lower temperatures and/or humidity. Therefore, this follows the Gloger's rule of becoming darker in colouration with the high rainfall and temperature in D. caerulescens. 
 Keywords: Dicrurus caerulescens, Colouration, Gloger's rule, Temperature, Precipitation 

Monitoring the condition of nitrogen-filled tires using weightless neural networks

The novelty of this paper revolves around monitoring the condition of nitrogen-filled tires through the fusion of features and utilization of weightless neural networks. A tire pressure monitoring system (TPMS) plays a crucial role in ensuring vehicle safety and comfort. Reckless driving, poor road conditions, continual operation, higher road friction and excessive load are certain factors that can degrade the longevity of tires. Such conditions can result in instantaneous fault attacks in tires raising a concern for safety and comfort. To apply instantaneous and accurate fault diagnosis, the present study leverages machine learning techniques through the integration of an adaptive and robust algorithm, namely, the Wilkes, Stonham and Aleksander Recognition Device (WiSARD) classifier. The experiment uses three types of features namely, statistical, histogram and autoregressive moving average (ARMA) features. The J48 decision tree algorithm was used to pinpoint the key attributes crucial for classification. Following this, the identified attributes were segregated into training and testing datasets, facilitating the evaluation of the WiSARD classifier. Hyperparameter tuning was carried out to achieve optimal value for maximizing classification accuracy and minimizing computational time. Among the features considered, ARMA features delivered the best test set accuracy of about 96.18%.

Prediction of Agricultural Commodity Prices using Big Data Framework

The agriculture sector plays a crucial role in the economy of Pakistan, contributing significantly to the Gross Domestic Product (GDP) and the employment rate. However, this sector faces challenges such as climate change, water scarcity, and low productivity, which have a direct impact on agricultural commodity prices. Accurate forecasting of commodity prices is essential for farmers, traders, and policymakers to make informed decisions and improve economic outcomes. This paper explores the use of a big data framework for agricultural commodity price forecasting in Pakistan, using a historical dataset on commodity prices in various Pakistani cities from 2007 to 2022 and Apache Spark to preprocess and clean the data. Based on historical spinach prices in Vehari City, the machine learning models Auto-Regressive Moving Average (ARIMA), Random Forest, and Long-Short-Term Memory (LSTM) were applied to price trends, and their performance was compared using Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE) and squared correlation coefficient (R2). LSTM outperformed ARIMA and Random Forest with a higher R2 value of 0.8 and the lowest MAE of 125.29. Such predictions can help farmers to effectively plan crop cultivation and traders to make well-informed decisions.

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