Autoregressive Integrated Moving Average Method Research Articles

CO2 capture, utilization, and storage (CCUS) storage site selection using DEMATEL-based Grey Relational Analysis and evaluation of carbon emissions with the ARIMA method.

Various systems are increasing gradually to reduce environmental concerns carbon capture, use, and storage (CCUS) systems are one of the most preferred processes in this context. Special areas such as depleted oil and gas reservoirs, aquifer reservoirs, and salt cave reservoirs are required needed to use this technology and, considering various criteria to decide on these areas is necessary. In this study, the problem of choosing the most suitable CCUS site selection for Turkey is discussed. In the study, for the alternative areas (Adıyaman, Aksaray, Diyarbakır, Afyon, and Tekirdağ) determined, 11 sub-assessment criteria (cost, CO2 state, regional risk, infrastructure availability, reservoir permeability, storage capacity, thickness reservoir, porosity, transportation availability, sustainability, facilities in operation by application) were discussed under CCUS main criterias (carbon capture, carbon storage, and carbon use). The DEMATEL method was used for the weights of the evaluation criteria. Unweighted gray relational analysis (GRA) method and DEMATEL-based GRA method were used to rank the alternatives. According to unweighted GRA results, Aksaray, Adıyaman, Afyon, Tekirdağ, and Diyarbakır decisions are obtained, respectively. According to the weighted GRA results, Aksaray, Adıyaman, Tekirdağ, Afyon, and Diyarbakır decisions are obtained, respectively. Calculation results demonstrated that Aksaray region is the most suitable alternative CCUS area for both methods. The CO amount values for the next period were estimated for this area and evaluated with the auto-regressive integrated moving average (ARIMA) method by the Minitab. By considering 1013 days of CO data, according to the MA(1) model estimation results were obtained values between 410.899 (μg/m3) and 459.909 (μg/m3). With the installation of the CCUS facility, this value could be checked again by examining it. The use of real data in the study increases the effectiveness of the proposed approach.

Comparative Study of the Error Trend and Seasonal Exponential Smoothing and ARIMA Model using COVID-19 Death Rate in Nigeria

In the last two years, COVID-19 had claimed millions of life in Nigeria and the world at large. It is an established global health emergency of our time and an ongoing threat faced by the world up till now. This study aims to determine the trend, fit an appropriate Error Trend and Seasonal (ETS) exponential smoothing and ARIMA model to the COVID-19 daily deaths in Nigeria. Dataset on the daily COVID-19 confirmed death cases were utilized in the study. The data was extracted from the Nigerian Centre for Disease Control (NCDC) online database from 10th July 2020 to 2nd December 2021. Autoregressive Integrated Moving Average (ARIMA) and twelve (12) (ETS) exponential smoothing techniques were compared based on the dataset. The performance of the ARIMA and ETS exponential smoothing methods was investigated using the Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), Hannan Quinn Information Criterion (HQC), and AMSE selection criteria. The best time series modeling for the coronavirus (COVID-19) epidemic in Nigeria was the ARIMA (0,1,0) because its model selection criteria showed that it had the lowest value of; AIC=2863.51, BIC= 2866.90, HQ = 2866.90, and AMSE = 0.55471. ARIMA (0,1,0) model is preferred among the thirteen (13) competing models based on daily confirmed deaths due to COVID-19 in Nigeria.

ANALYSIS AND COMPARING FORECASTING RESULTS USING TIME SERIES METHOD TO PREDICT SALES DEMAND ON COVID-19 PANDEMIC ERA

The Covid-19 pandemic has made uncertainty in demand very high; there have been many changes in demand due to changes in the market and people's buying methods. So that forecasting accuracy is significant for every industry, at least the forecast that is closest to the conditions faced by the company so that the company does not lose money due to forecasting errors. Time series is a widely used model for forecasting using past data. This study aims to minimize forecasting errors by analyzing which demand forecasting model is most suitable for demand conditions based on historical data on demand for masterbatch products. The method used in this study is a time series model, which consists of the season naive method, holt exponential smoothing, exponential triple smoothing, and autoregressive integrated moving average (ARIMA). Data processing is done using Rstudio software. The results show that the ARIMA method (2,1,0) (1,1,0) is the best because it has the smallest error rate value with case studies and exact data; the standard error size values used are ME, RMSE, MAE, MPE, MAPE, MASE, and ACF1. This study analyzes forecasting during the Covid-19 pandemic using time series and compares them to find the best results. Then the results of this study can be used as a reference by companies and researchers in determining the model used to make forecasts.

Predicting sales during COVID using Machine Learning Techniques

Abstract: The purpose of this study is to compare VAR, ARIMA and SARIMA methods in an attempt to generate sales forecasting in Store xyz with high accuracy. This study will compare the results of sales forecasting with time series forecasting model of Vector Auto Regression (VAR), Autoregressive Integrated Moving Average (ARIMA) and Seasonal Autoregressive Integrated Moving Average (SARIMA). VAR or ARIMA model still accurate when the time series data is only in a short period, these models is accurate on short period forecasting but less accurate on long period forecasting. Meanwhile Seasonal Autoregressive Integrate Moving Average is more accurate on forecasting seasonal time series data, either it’s pattern shows trend or not all three models are compared with forecasting data showing seasonal patterns. The data used is the data of super mart retail store, sales from 2017 to 2022. Accuracy level of each model is measured by comparing the percentage of forecasting value with the actual value. This value is called Mean Absolute Deviation (MAD). Based on the comparison result, the best model with the smallest MAD value is SARIMA model (0,1,0) (0,1,0)12 with MAD value 0.122. From the comparison results can be concluded that the SARIMA model is optimal to be used as a model for further forecasting Keywords: Machine Learning, sales prediction, ARIMA, SARIMA, VAR, PYTHON, Anaconda navigator, Jupiter notebook.

On Forecasting Nigeria’s GDP: A Comparative Performance of Regression with ARIMA Errors and ARIMA Method

This paper examines the application of autoregressive integrated moving average (ARIMA) model and regression model with ARIMA errors for forecasting Nigeria’s GDP. The data used in this study are collected from the official website of World Bank for the period 1990-2019. A response variable (GDP) and four predictor variables are used for the study. The ARIMA model is fitted only to the response variable, while regression with ARIMA errors is fitted on the data as a whole. The Akaike Information Criterion Corrected (AICc) was used to select the best model among the selected ARIMA models, while the best model for forecasting GDP is selected using measures of forecast accuracy. The result showed that regression with ARIMA(2,0,1) errors is the best model for forecasting Nigeria’s GDP.

Multi-temporal analysis of groundwater depletion-induced land subsidence in Central Ganga Alluvial plain, Northern India

This study explores the Persistent Scatterer Interferometry technique to identify the developing pattern of groundwater depletion-induced land subsidence in Lucknow, northern India. The results show the development of two significant subsidence zones, one each in the north and south of Lucknow. The situation is potentially alarming as ∼9.3% (∼27.6 km2) of the total area is under the expression of a weak degree of subsidence (∼10–40 mm/yr). The detected displacement trends substantially correspond to the constant dwindling of groundwater levels in the city, further indicating that the first unconfined aquifer is under high stress and faces a continuous storage capacity loss with time. The time-series displacement trends have been later forecasted using the Autoregressive Integrated Moving Average method, indicating the city might face subsidence at the rate of ∼25–40 mm/yr. The findings of this study are significant to take precise mitigation measures in the areas facing continuous groundwater depletion on a long-term basis.

The Prediction of Influenza-like Illness and Respiratory Disease Using LSTM and ARIMA.

This paper proposed the forecasting model of Influenza-like Illness (ILI) and respiratory disease. The dataset was extracted from the Taiwan Environmental Protection Administration (EPA) for air pollutants data and the Centers for Disease Control (CDC) for disease cases from 2009 to 2018. First, this paper applied the ARIMA method, which trained based on the weekly number of disease cases in time series. Second, we implemented the Long short-term memory (LSTM) method, which trained based on the correlation between the weekly number of diseases and air pollutants. The models were also trained and evaluated based on five and ten years of historical data. Autoregressive integrated moving average (ARIMA) has an excellent model in the five-year dataset of ILI at 2564.9 compared to ten years at 8173.6 of RMSE value. This accuracy is similar to the Respiratory dataset, which gets 15,656.7 in the five-year dataset and 22,680.4 of RMSE value in the ten-year dataset. On the contrary, LSTM has better accuracy in the ten-year dataset than the five-year dataset. For example, on average of RMSE in the ILI dataset, LSTM has 720.2 RMSE value in five years and 517.0 in ten years dataset. Also, in the Respiratory disease dataset, LSTM gets 4768.6 of five years of data and 3254.3 of the ten-year dataset. These experiments revealed that the LSTM model generally outperforms ARIMA by three to seven times higher model performance.

Comparison of Fuzzy Time Series Methods and Autoregressive Integrated Moving Average (ARIMA) for Inflation Data

This study compares the Fuzzy Time Series (FTS) method with the Autoregressive Integrated Moving Average (ARIMA) method on time series data. These two methods are often used in predicting future data. Forecasting or time-series data analysis is used to analyze data in the form of time series. In this study, Indonesian inflation data was used to be analyzed in comparing the FTS and ARIMA methods. Inflation is one of the economic indicators used to measure the success of a country's economy. If the inflation rate is low and stable, it will stimulate economic growth. This inflation value is calculated every month where the value can decrease and increase from one period to another. Comparison of the FTS and ARIMA methods is seen in the error value generated by the two methods. A method can be better than other methods if the value of the resulting forecast error is smaller. In this study, Mean Squared Error (MSE) and Mean Absolute Percentage Error (MAPE) were used to see the magnitude of the error value of the two methods on the fives data testing used. The results obtained in this study are the results of Indonesia's inflation forecast for the period January to May 2021 using the FTS method, respectively, at 0.57%, 0.375%, 0.2%, 0.2%, and 0.1125%, while the forecast results using the ARIMA method, respectively. Of 0.3715848%, 0.2362817%, 0.1508295%, 0.1731906%, and 0.2432851% and the results of calculating the size of error using MSE and MAPE indicate that the ARIMA method with the model ARIMA(3,0,0) is better at predicting inflation data in Indonesia with a value of MSE of 0.009 and MAPE of 64.987% compared to the FTS method resulted in MSE values of 0.047 and MAPE of 132.548%.

Improving membrane filtration performance through time series analysis

For ultrafiltration, and membrane filtration more generally, the quantitative determination of the modes of fouling remains a subject of great interest. Herein an integral method for determining the modes from a time series of volumetric flux Jleft(tright) is given and illustrated with previously published filtration data of bergamot juice (Ruby-Figueroa et al (J Membr Sci 524:108-116, 2017)). The integral method of fouling analysis has the potential to become the cornerstone of a robust empirical process. In addition to determining, in a clear-cut manner, the point at which there is a switch from one mode to another, the robust methodology yields characteristic Jleft(tright) equation for each mode that are an excellent fit to the data. The emphasis is upon the creation of a robust methodology which is best viewed as being a semi-empirical method that is indicative of the modes of fouling. For the example chosen, the initial 4 L/m2 generates some pore blocking after which the main mode of fouling is cake build-up. The variation of overall resistance with time is also informative and analysis of this series was used to check the result for the initial phase of fouling as determined from the time series of volumetric flux. A comparison against the ARIMA (Autoregressive integrated moving average) method, which has never been previously undertaken, is given herein. The integral method of fouling analysis was found to be superior, in part because of the quality of fit to the data and in part because it enables one to establish whether the initial fouling is different in character from the subsequent fouling. Having this information can improve membrane selection and overall membrane filtration performance.

Comparative Analysis of The Growth of School Students Using Autoregressive Integrated Moving Average Methods Analisis

This study aims to analyze and predict the number of Elementary School Students using Autoregressive Integrated Moving Average (ARIMA) method using data from the last 17 years, case studies in three provinces namely Bali, West Nusa Tenggara (NTB), and East Nusa Tenggara (NTT). This type of research is quantitative by comparing the final value on the first graph to the fourth graph to analyze on the graph what the predictive value is most accurate. Based on the results of the simulation of the number of elementary school students in Bali, NTB, and NTT provinces using the G-MFS application program and mathematical model calculations that the predicted results in 2021 on the data of the number of elementary school students in Bali province amounted to 417,805.40 with a percentage decrease of 0.1%, then the predicted result in the data of the number of elementary school students in NTB province of 512,381.76 with a percentage increase of 1.0%. The predicted result on the data of the number of elementary school students in NTT province amounted to 705,335.11 with an increase of 1.0%. The results of the forecasting of the number of elementary school students are expected to provide important information for the government to improve development in the education sector, especially at the elementary school education level in one way that is to improve the quality of educational infrastructure and many more developments that need to be done by the number of students in the future.

An integrated approach for reducing spatially coupled water-shortage risks of Beijing-Tianjin-Hebei urban agglomeration in China

Growing agricultural and economic activities have brought tremendous pressure to regional water resources management. Considering the interactions of water resources allocation among adjacent regions with connected water systems, it is necessary to pay attention to the unification of socio-economic development and environmental protection between the regions. This is of significance to many agglomeration areas that are composed of multiple large cities such as the Beijing-Tianjin-Hebei urban agglomeration (BTH) in China. In this research, an ensemble approach was proposed to tackle water resources allocation problems under the constraints of water resources to reflect interdependence of the composed three areas, i.e., Beijing, Tianjin and Hebei. The approach included ARIMA (Autoregressive Integrated Moving Average) method, ANN (Artificial Neural Network) modelling, copula functions, and multi-objective chance-constrained programming model. It can 1) reflect the variations characteristic of regional water supply and demands, 2) solve the interactions between multi-regional water resources, and 3) obtain desired water resources allocation strategies under multi- scenario and scale conditions. Then, the approach was applied in typical regions of China, i.e., Beijing-Tianjin-Hebei urban agglomeration. Considering 27 scenarios of the nexus water shortage risk, multi-objective joint opportunity constraint programming model aimed at maximizing economic benefits and ecological benefits, in combination with economic, agricultural, water resources and environmental constraints. As the optimal strategies indicated, the maximum GDP in BTH would remain at 8514 billion yuan. The minimum amount of water allocation and wastewater discharge would be 24–26 billion m3 and 2100–2101 million m3 respectively.

Modeling the British Pound Sterling to Nigerian Naira Exchange Rate During the Covid-19 Pandemic

The British Pound Sterling (GBP) to Nigerian Naira (NGN) exchange rate has been grossly affected by the Coronavirus 2019 (Covid-19) pandemic. It has become pertinent to identify robust models that will help to cope with the variability associated with the pandemic. Many original studies found the ARIMA method to be highly useful in modeling and forecasting exchange rates. However, not much work has been done on modeling the GBP and NGN exchange rate during the covid-19 pandemic using machine learning models. This study focuses on modeling the exchange rate between the GPB and NGN during the period of the Covid-19 pandemic by adopting the process of model comparison using the Artificial Neural Network (ANN), Autoregressive Integrated Moving Average (ARIMA), and Random Forest models to obtain an optimal model and forecasts from the model. Secondary data of the GBP to NGN exchange rate within the period of the Covid-19 pandemic from exchangerate.org.uk were used. The two machine learning models (ANN and random forest) performed better than the ARIMA model. The RF, though performed well in the training set, was outperformed in the test set by the ANN model. The ANN model was chosen to model and forecast the GBP and NGN exchange rate during the Covid-19 pandemic. The predicted fall in the GBP to NGN exchange rate to 570 by December 2021 and 575 by September 2022 using the ANN model will have a huge effect on the economy of the country as the country depends largely on imported goods. The Government and policymakers must put in place structural measures that will avoid the looming crisis.

Analysis and Prediction of Sulfate Erosion Damage of Concrete in Service Tunnel Based on ARIMA Model.

Sulfate erosion is a major cause of concrete durability deteriorations, especially for the service tunnels that suffer sulfate erosion for a long time. Accurately predicting the concrete damage failure under sulfate erosion has been a challenging problem in the evaluation and maintenance of concrete structures. Here we design the dry–wet cycle test of service tunnel concrete under sulfate erosion and analyze the Elastic relative dynamic modulus (Erd) and mass under 35 times cycle periods. Then we develop an autoregressive integrated moving average (ARIMA) prediction model linking damage failure to Erd and mass. The results show that the deterioration of concrete first increased and then decreased with an extension of the dry–wet cycle period. Moreover, based on a finite set of training data, the proposed prediction approach shows high accuracy for the changes of concrete damage failure parameters in or out of the training dataset. The ARIMA method is proven to be feasible and efficient for predicting the concrete damage failure of service tunnels under sulfate erosion for a long time.

Quality Control of Glehniae Radix, the Root of Glehnia Littoralis Fr. Schmidt ex Miq., Along its Value Chains.

Glehniae Radix (GR) is one of the major medicinal materials in China. The global demand for GR, especially in Asian countries, is constantly increasing, and the supply of wild medicinal materials falls short of the demand. Previous studies have shown that the production and processing modes of different value chains (VCs) impact the quality of medicinal materials. After 4 years of field and market research, this study includes interviews with stakeholders in the VCs, integrates different types of VCs, and further analyzes the VCs. GR characteristics were also assessed; the length and upper-middle diameter of the collected samples were measured, and the effective components of the samples were determined to rank the GR samples according to their quality. The effective components were further analyzed by the K-means clustering method. Concomitantly, the local price (the sales price of the place where the medicinal materials are produced) and market price (the sale price of medicinal materials in the market) of GR in Chifeng, Inner Mongolia, and Anguo, Hebei, were documented, and the ARIMA (Autoregressive Integrated Moving Average) method was used to predict the GR price. Ten VCs are summarized in this article. The results showed that the income of the staff at the beginning of the VC is inadequate. Regarding GR origin, Inner Mongolia GR showed higher quality than that of other areas. As a result, the price of medicinal materials is relatively high, which corresponds to the market price of the survey. The forecast results showed that the market price of GR would increase slightly in the future, which could provide reference for the selection of medicinal materials cultivation in the future. Through the study, it was found that the vertical integration in the VCs of GR could guarantee not only the benefit of the growers but also the traceability of the medicinal materials, which further guarantees the quality of the medicinal materials. However, the complex relationship between the cultivation area and the quality of the medicinal materials is not clear, which should be addressed in future research.

PERAMALAN PRODUKSI BANGGAI CARDINALFISH MENGGUNAKAN METODE AUTOREGRESSIVE INTEGRATED MOVING AVERAGE (ARIMA)

The ARIMA (Autoregressive Integrated Moving Average) method is a very appropriate method to use for the short term because the ARIMA method has very accurate accuracy. And also determine a good relationship between the variables to be forecasted with the value used for forecasting. This study uses the ARIMA method. The purpose of the study was to predict the production of Banggai Cardinalfish (Pterapogon kauderni) which is an ornamental fish commodity that is in great demand. High market demand and production predictions are able to provide the market, becoming important information so that potentials and opportunities can be exploited. The accuracy of the resulting forecast is calculated using the MSE (Mean Squared Error) and MAE (Mean Absolute Error) values. Forecasting results from Banggai Cardinalfish production using the ARIMA method were 830.33 in May 2017 with the resulting MAPE value of 176.93 and MAE of 975.23.

The forecasting model of discharge at Brantas sub-basin using autoregressive integrated moving average (ARIMA) and decomposition methods

A watershed is a combination of several rivers and tributaries with certain boundaries that function to drain rainwater into a lake or a sea. One of the hydrological data contained in the watershed is discharge data. If there is incomplete discharge data, it must be extended based on historical data. ARIMA and decomposition are methods that can predict time series data. The purposes of this research are to determine the historical discharge patterns of Brantas Sub-basin, to know the discharge forecasting model of Brantas Sub-basin, to know the results of forecasting Brantas Sub-basin discharge, and to compare the accuracy between ARIMA and decomposition methods. The accuracy is obtained by calculating MSE and RMSE values. The best method is a method that has the smallest MSE and RMSE values. The results of the research showed that Brantas Sub-basin discharge data in 2007-2017 has a seasonal pattern. The best ARIMA model is ARIMA (0,0,3)(1,0,1)12 model, while the best decomposition model is the additive decomposition model. The Decomposition method has better accuracy than the ARIMA method in predicting discharge of Brantas Sub-basin.

FORECASTING STOCK PRICES ON THE LQ45 INDEX USING THE VARIMAX METHOD

Forecasting using the Autoregressive Integrated Moving Average (ARIMA) method is not appropriate to predict more than one stock price because this method is only able to model one dependent variable. Therefore, to expect more than one stock prices, the ARIMA method expansion can be used, namely the Vector Autoregressive Integrated Moving Average (VARIMA) method. Furthermore, this research will discuss forecasting stock prices on the LQ45 index using the Vector Autoregressive Integrated Moving Average with Exogenous Variable (VARIMAX) method. Then, after the initial model formation process, the best model is the VARIMAX (0,1,2) model. Finally, the results of this study using the VARIMAX (0,1,2) model obtained the predictive value of the prices and the error values of stocks on the LQ45 index.

Convolutional Neural Network–Component Transformation (CNN–CT) for Confirmed COVID-19 Cases

The COVID-19 disease constitutes a global health contingency. This disease has left millions people infected, and its spread has dramatically increased. This study proposes a new method based on a Convolutional Neural Network (CNN) and temporal Component Transformation (CT) called CNN–CT. This method is applied to confirmed cases of COVID-19 in the United States, Mexico, Brazil, and Colombia. The CT changes daily predictions and observations to weekly components and vice versa. In addition, CNN–CT adjusts the predictions made by CNN using AutoRegressive Integrated Moving Average (ARIMA) and Exponential Smoothing (ES) methods. This combination of strategies provides better predictions than most of the individual methods by themselves. In this paper, we present the mathematical formulation for this strategy. Our experiments encompass the fine-tuning of the parameters of the algorithms. We compared the best hybrid methods obtained with CNN–CT versus the individual CNN, Long Short-Term Memory (LSTM), ARIMA, and ES methods. Our results show that our hybrid method surpasses the performance of LSTM, and that it consistently achieves competitive results in terms of the MAPE metric, as opposed to the individual CNN and ARIMA methods, whose performance varies largely for different scenarios.

Wind Speed Prediction based on Spatio-Temporal Covariance Model Using Autoregressive Integrated Moving Average Regression Smoothing

It is essential to enhance the ability of wind speeds forecasting for wind energy and wind resource planning. For this purpose, a hybrid strategy has been proposed based on spatio-temporal covariance model which combined the spatio-temporal ordinary kriging (STOK) technology with autoregressive integrated moving average (ARIMA) regression smoothing method. This is because wind speed time series exhibits a long-term dependency. In the case study, both STOK method and ARIMA method are employed and their performances are compared. The ARIMA model can obtain a necessary and sufficient smoothing condition for them to be smoothed. Meanwhile, further theoretical analysis is provided to discuss why the STOK method is potentially more accurate than the ARIMA method for wind speed time series prediction. Results show that the proposed method outperforms the Non-Sep-Gneiting model by 9% and 7.2% in terms of mean absolute error (MAE) and root-mean-square error (RMSE).

Prediction Of Tiger Shrimp Supply Using Time Series Anlysis Method Case Study CV.Surya Perdana Benur

Prediction of demand for tiger shrimp buyers using data from the company CV. Surya Perdana Benur. The process is carried out with the models in the Autoregressive Integrated Moving Average method. Tiger shrimp is a marine animal that is now widely cultivated by big company in Indonesia. Tiger shrimp has important economic value, so its existence must be maintained as part of Indonesian germplasm. The problem now faced by many tiger shrimp companies is the inadequate availability of goods for consumers. This time series data method is useful for predicting the availability of goods for consumers who want to buy goods at the company CV. Surya Perdana Benur. This time series data method is useful for predicting the availability of goods for consumers who want to buy goods at the company CV. Surya Perdana Benur. Autoregressive (AR), MovingAverage (MA), and Autoregressive Integrated Moving Average (ARIMA) model and will be evaluated through Mean Absolute Percent Error (MAPE). The initial process that will be carried out after the data is processed is model identification, estimation of model parameters, residual inspection, using forecasting models if the model has been fulfilled will be evaluated using MAPE until the results come out 14875.593875 to be able to predict the next buyer demand.