Water Use Trend at Universiti Teknologi Malaysia: Application of ARIMA Model

Water Consumption ◽

Moving Average ◽

Arima Model ◽

Information Criterion ◽

University Campus ◽

Partial Autocorrelation Function

Kertas kerja ini menerangkan aplikasi kaedah permodelan (ARIMA) bagi mewakili perilaku penggunaan air di kampus Universiti Teknologi Malaysia. Menggunakan fungsi–fungsi ACF, PACF dan AIC, siri masa penggunaan air bulanan di kampus UTM boleh dinyatakan dalam model ARIMA (2,0,0). Anggaran parameter model ø1 dan ø2 ialah 0.2747 dan 0.4194. Keadaan tersebut menggambarkan bahawa penggunaan air pada bulan semasa tidak semestinya dipengaruhi dengan tepat oleh kadar penggunaan air pada bulan sebelumnya. Analisis juga menunjukkan model ARIMA (2,0,0) boleh diguna sebagai model ramalan guna air di kampus universiti. Kata kunci: Guna air, kampus universiti, siri masa, model ARIMA The paper describes the application of autoregressive integrated moving average (ARIMA) model to represent water use behaviour at Universiti Teknologi Malaysia (UTM) campus. Using autocorrelation function (ACF), partial autocorrelation function (PACF), and Akaike’s Information Criterion (AIC), monthly campus water use series can be best presented using ARIMA (2,0,0) model. The estimated parameter of the model ø1 and ø2 are 0.2747 and 0.4194 respectively. This implies that water consumption in UTM campus at the present month is not necessarily influenced by water consumption of immediate previous month. Analysis shows that ARIMA (2,0,0) model provides a reasonable forecasting tool for campus water use. Key words: Water use, university campus, time series, ARIMA model

Advances in Medical Technologies and Clinical Practice - Machine Learning and Data Analytics for Predicting, Managing, and Monitoring Disease ◽

Predicting Daily Confirmed COVID-19 Cases in India

10.4018/978-1-7998-7188-0.ch003 ◽

2021 ◽

pp. 34-51

Author(s):

Sudip Singh

Keyword(s):

Time Series ◽

Root Mean Square Error ◽

Moving Average ◽

Arima Model ◽

Family Welfare ◽

Mean Square ◽

Univariate Time Series ◽

Auto Regressive

India, with a population of over 1.38 billion, is facing high number of daily COVID-19 confirmed cases. In this chapter, the authors have applied ARIMA model (auto-regressive integrated moving average) to predict daily confirmed COVID-19 cases in India. Detailed univariate time series analysis was conducted on daily confirmed data from 19.03.2020 to 28.07.2020, and the predictions from the model were satisfactory with root mean square error (RSME) of 7,103. Data for this study was obtained from various reliable sources, including the Ministry of Health and Family Welfare (MoHFW) and http://covid19india.org/. The model identified was ARIMA(1,1,1) based on time series decomposition, autocorrelation function (ACF), and partial autocorrelation function (PACF).

ANALISIS TIME SERIES DENGAN METODE ARIMA DAN APLIKASINYA

Jurnal Aplikasi Teknologi Informasi dan Manajemen (JATIM) ◽

10.31102/jatim.v1i2.972 ◽

2020 ◽

Vol 1 (2) ◽

pp. 26-36

Author(s):

Fathorrozi Ariyanto ◽

Moh. Badri Tamam

Keyword(s):

Time Series ◽

Moving Average ◽

Model Time Series

Model time series yang sangat terkenal adalah model Autoregressive Integrated Moving Average (ARIMA) yang dikembangkan oleh George E. P. Box dan Gwilym M. Jangkins. Model time series ARIMA menggunakan teknik-teknik korelasi. Identifikasi model bisa dilihat dari ACF (Autocorrelation Function) dan PACF (Partial Autocorrelation Function) suatu deret waktu. Tujuan model ARIMA dalam penelitian ini adalah untuk menemukan suatu model yang akurat yang mewakili pola masa lalu dan masa depan dari suatu data time series. Pada penelitian ini, Penulis akan menganalisis penurunan algoritma suatu metode peramalan yang disebut metode peramalan ARIMA Kemudian menerapkan metode tersebut pada data riil yaitu data produksi air di PDAM Pamekasan dengan bantuan komputer dan software SPSS, yang nantinya akan diterapkan di dalam memberikan informasi dan analisis yang akurat terhadap perusahaan PDAM Pamekasan.Dari hasil pembahasan diperoleh rumus ARIMA yang berbentuk: Profit=+Y+Z, kemudian dari hasil penerapan data riil yaitu pada data produksi air di PDAM Pamekasan diperoleh model ARIMA (1 0 0) (0 0 1) sebagai model terbaik. Dengan model :

Self-Identification ResNet-ARIMA Forecasting Model

WSEAS TRANSACTIONS ON SYSTEMS AND CONTROL ◽

10.37394/23203.2020.15.21 ◽

2020 ◽

Vol 15 ◽

Keyword(s):

Time Series ◽

Time Series Data ◽

Moving Average ◽

Arima Model ◽

Model Identification ◽

Series Data ◽

Percentage Error ◽

Forecasting Model ◽

Partial Autocorrelation Function

The challenging endeavor of a time series forecast model is to predict the future time series data accurately. Traditionally, the fundamental forecasting model in time series analysis is the autoregressive integrated moving average model or the ARIMA model requiring a model identification of a three-component vector which are the autoregressive order, the differencing order, and the moving average order before fitting coefficients of the model via the Box-Jenkins method. A model identification is analyzed via the sample autocorrelation function and the sample partial autocorrelation function which are effective tools for identifying the ARMA order but it is quite difficult for analysts. Even though a likelihood based-method is presented to automate this process by varying the ARIMA order and choosing the best one with the smallest criteria, such as Akaike information criterion. Nevertheless the obtained ARIMA model may not pass the residual diagnostic test. This paper presents the residual neural network model, called the self-identification ResNet-ARIMA order model to automatically learn the ARIMA order from known ARIMA time series data via sample autocorrelation function, the sample partial autocorrelation function and differencing time series images. In this work, the training time series data are randomly simulated and checked for stationary and invertibility properties before they are used. The result order from the model is used to generate and fit the ARIMA model by the Box-Jenkins method for predicting future values. The whole process of the forecasting time series algorithm is called the self-identification ResNet-ARIMA algorithm. The performance of the residual neural network model is evaluated by Precision, Recall and F1-score and is compared with the likelihood basedmethod and ResNET50. In addition, the performance of the forecasting time series algorithm is applied to the real world datasets to ensure the reliability by mean absolute percentage error, symmetric mean absolute percentage error, mean absolute error and root mean square error and this algorithm is confirmed with the residual diagnostic checks by the Ljung-Box test. From the experimental results, the new methodologies of this research outperforms other models in terms of identifying the order and predicting the future values.

Time Series ARIMA Model for Prediction of Daily and Monthly Average Global Solar Radiation: The Case Study of Seoul, South Korea

Symmetry ◽

10.3390/sym11020240 ◽

2019 ◽

Vol 11 (2) ◽

pp. 240 ◽

Cited By ~ 20

Author(s):

Mohammed Alsharif ◽

Mohammad Younes ◽

Jeong Kim

Keyword(s):

Time Series ◽

Solar Radiation ◽

South Korea ◽

Moving Average ◽

Arima Model ◽

Sarima Model ◽

Radiation Data ◽

Auto Regressive

Forecasting solar radiation has recently become the focus of numerous researchers due to the growing interest in green energy. This study aims to develop a seasonal auto-regressive integrated moving average (SARIMA) model to predict the daily and monthly solar radiation in Seoul, South Korea based on the hourly solar radiation data obtained from the Korean Meteorological Administration over 37 years (1981–2017). The goodness of fit of the model was tested against standardized residuals, the autocorrelation function, and the partial autocorrelation function for residuals. Then, model performance was compared with Monte Carlo simulations by using root mean square errors and coefficient of determination (R2) for evaluation. In addition, forecasting was conducted by using the best models with historical data on average monthly and daily solar radiation. The contributions of this study can be summarized as follows: (i) a time series SARIMA model is implemented to forecast the daily and monthly solar radiation of Seoul, South Korea in consideration of the accuracy, suitability, adequacy, and timeliness of the collected data; (ii) the reliability, accuracy, suitability, and performance of the model are investigated relative to those of established tests, standardized residual, autocorrelation function (ACF), and partial autocorrelation function (PACF), and the results are compared with those forecasted by the Monte Carlo method; and (iii) the trend of monthly solar radiation in Seoul for the coming years is analyzed and compared on the basis of the solar radiation data obtained from KMS over 37 years. The results indicate that (1,1,2) the ARIMA model can be used to represent daily solar radiation, while the seasonal ARIMA (4,1,1) of 12 lags for both auto-regressive and moving average parts can be used to represent monthly solar radiation. According to the findings, the expected average monthly solar radiation ranges from 176 to 377 Wh/m2.

COVID-19 ARIMA Model Forecast: Confirmed, Mortality & Recovered Cases in 13 Caribbean Countries (Preprint)

10.2196/preprints.20792 ◽

2020 ◽

Author(s):

Debjyoti Talukdar ◽

Dr. Vrijesh Tripathi

Keyword(s):

Time Series ◽

Moving Average ◽

Arima Model ◽

Information Criterion ◽

Local Health ◽

Health Administration ◽

Model Forecast ◽

Time Period ◽

The Impact

BACKGROUND Rapid spread of SARS nCoV-2 virus in Caribbean region has prompted heightened surveillance with more than 350,000 COVID-19 confirmed cases in 13 Caribbean countries namely Antigua and Barbados, Bahamas, Barbados, Cuba, Dominica, Dominican Republic, Grenada, Haiti, Jamaica, Saint Kitts and Nevis, Saint Lucia, Saint Vincent and the Grenadines, Trinidad and Tobago. OBJECTIVE The aim of our study is to analyze the impact of coronavirus (SARS nCoV-2) in 13 Caribbean countries in terms of confirmed cases, number of deaths and recovered cases. Current and projected forecasts using advanced autoregressive integrated moving average (ARIMA) models will enable local health organisations to plan future courses of action in terms of lockdown and managing essential public services. METHODS The study uses the auto regressive integrated moving average (ARIMA) model based upon time series pattern as per data retrieved from John Hopkins University, freely accessible on public domain and used for research and academic purposes. The data was analyzed using STATA 14 SE software between the time period - Jan 22, 2020 till May 27, 2020 using ARIMA time series analysis. It involves generalizing an autoregressive moving average model to better understand the data and predict future points in the time series until June 15, 2020. RESULTS The results show the predicted trend in terms of COVID-19 confirmed, mortality and recovered cases for 13 Caribbean countries. The projected ARIMA model forecast for the time period - May 25, 2020 to May 31, 2020 show 20278 (95% CI 19433.21 - 21123.08) confirmed cases, 631 (95% CI 615.90 - 646.51) deaths and 11501 (95% CI 10912.45 - 12089) recovered cases related to SARS nCoV-2 virus. The final ARIMA model chosen for confirmed COVID-19 cases, number of deaths and recovered cases are ARIMA (4,2,2), ARIMA (2,1,2) and ARIMA (4,1,2) respectively. All chosen models were compared with other models in terms of various factors like AIC/BIC (Akaike Information Criterion/Bayesian Information Criterion), log likelihood, p-value significance, coefficient < 1 and 5% significance. The autocorrelation function (ACF) and partial autocorrelation function (PACF) graphs were plotted to reduce bias and select the best fitting model. CONCLUSIONS As per the results of the forecasted COVID-19 models, there is a steady rise in terms of confirmed, recovered and mortality cases during the time period March 1, 2020 until May 27, 2020. It shows an increasing trend for confirmed and recovered COVID-19 cases and slowing of the number of mortality cases over a period of time. The predicted model will help the local health administration to devise public policies in terms of awareness measures, lockdown and essential health services accordingly.

Forecasting the spread and total size of confirmed and discharged cases of COVID-19 in Nigeria using an ARIMA model

Statistical Journal of the IAOS ◽

10.3233/sji-200758 ◽

2021 ◽

pp. 1-6

Author(s):

S. Agboola ◽

P. Niyang ◽

O. Olawepo ◽

W. Ukponu ◽

S. Niyang ◽

...

Keyword(s):

Moving Average ◽

Arima Model ◽

Total Size ◽

Daily Data ◽

The Government ◽

Global Threat ◽

Mandatory Use

Coronavirus disease 2019 (COVID-19) has been considered a global threat spreading to Nigeria and posing major public health threats and concerns. This led to the introduction of internationally acceptable non-pharmaceutical interventions (NPI) such as lockdowns, social distancing, and mandatory use of face masks by the Nigerian government to curtail the disease. This study aims to develop an Autoregressive Integrated Moving Average (ARIMA) model to predict COVID-19 cases vis Total Confirmed Cases (TCC) and Total Discharged Cases (TDC) in Nigeria based on the daily data obtained from the Nigeria Centre for Diseases Control (NCDC) from 27th February 2020 to 6th June 2020. The autocorrelation function (ACF), and partial autocorrelation function (PACF) were used to determine the constructed model. An ARIMA model was developed to predict the trend of TCC and TDC for the next 200 days. Forecasting was done using the constructed models. The finding shown that TCC increased to 50,225 with a CI between 29,425 to 100,450 and TDC to 20,186 with CI between 11,106 to 40,366 approximately. The result shows a significant increase in both TCC and TDC from COVID-19 which should guide the government roll out and management of the different NPI and policies to contain the virus.

Predicting Economic Performance of Bangladesh using Autoregressive Integrated Moving Average (ARIMA) model.

10.47260/jafb/1125 ◽

2021 ◽

pp. 129-148

Author(s):

Raad Mozib Lalon ◽

Nusrat Jahan

Keyword(s):

Economic Performance ◽

Goodness Of Fit ◽

Moving Average ◽

Arima Model ◽

Unit Root Test ◽

Information Criterion ◽

Gdp Growth ◽

Q Model

This paper attempts to forecast the economic performance of Bangladesh measured with annual GDP data using an Autoregressive Integrated Moving Average (ARIMA) Model followed by test of goodness of fit using AIC (Akaike Information Criterion) and BIC (Bayesian Information Criterion) index value among six ARIMA models along with several diagnostic tests such as plotting ACF (Autocorrelation Function), PACF (Partial Autocorrelation Function) and performing Unit Root Test of the Residuals estimated by the selected forecasting ARIMA model. We have found the appropriate ARIMA (1,0,1) model useful in predicting the GDP growth of Bangladesh for next couple of years adopting Box-Jenkins approach to construct the ARIMA (p,r,q) model using the GDP data of Bangladesh provided in the World Bank Data stream from 1961 to 2019. JEL classification numbers: B22, B23, C53. Keywords: GDP growth, ACF, PACF, Stationary, ARIMA (p,r,q) model, Forecasting.

Optimization of ARIMA Forecasting Model using Firefly Algorithm

IJCCS (Indonesian Journal of Computing and Cybernetics Systems) ◽

10.22146/ijccs.37666 ◽

2019 ◽

Vol 13 (2) ◽

pp. 127 ◽

Cited By ~ 1

Author(s):

Ilham Unggara ◽

Aina Musdholifah ◽

Anny Kartika Sari

Keyword(s):

Time Series ◽

Firefly Algorithm ◽

Moving Average ◽

Time Series Prediction ◽

Arima Model ◽

Information Criterion ◽

Forecasting Model ◽

Optimal Model ◽

Foreign Tourist

Time series prediction aims to control or recognize the behavior of the system based on the data in a certain period of time. One of the most widely used method in time series prediction is ARIMA (Autoregressive Integrated Moving Average). However, ARIMA has a weakness in determining the optimal model. firefly algorithm is used to optimize ARIMA model (p, d, q). by finding the smallest AIC (Akaike Information Criterion) value in determining the best ARIMA model. The data used in the study are daily stock data JCI period January 2013 until August 2016 and data of foreign tourist visits to Indonesia period January 1988 to November 2017.Based on testing, for JCI data, obtained predicted results with Box-Jenkins ARIMA model produces RMSE 49.72, whereas the prediction with the ARIMA Optimization model yielded RMSE 49.48. For the data of Foreign Tourist Visits, the predicted results with the Box-Jenkins ARIMA model resulted in RMSE 46088.9, whereas the predicted results with ARIMA optimization resulted in RMSE 44678.4. From these results it can be concluded that the optimization of ARIMA model with Firefly Algorithm produces better forecasting model than ARIMA model without Optimization.

Forecast and Analyze the Revenue of Biman Bangladesh Airlines Limited Based on ARIMA Model

Asian Journal of Advanced Research and Reports ◽

10.9734/ajarr/2020/v10i430248 ◽

2020 ◽

pp. 12-20

Author(s):

Sumaiya Rahman ◽

Shohel Ahmed ◽

Tahrima Faruq

Keyword(s):

Time Series ◽

Moving Average ◽

Arima Model ◽

Information Criterion ◽

Expenditure Data ◽

Modeling And Forecasting ◽

Limited Company ◽

R Programming ◽

Best Fit

Background: As a public limited company in Bangladesh, Biman Bangladesh Airlines Limited has been struggling to establish itself as a profitable company after taking many initiatives. The work presented in this article constitutes a contribution to modeling and forecasting the financial positionof Biman Bangladesh by using a time series approach.Methodology: The article demonstrates how the income and expenditure data could be utilized to forecast future profit scenarios by developing several Autoregressive Integrated Moving Average (ARIMA) time series with the regression model. Utilizing the Akaike Information Criterion (AIC) values, we identify the best fit ARIMA model and use this to forecast the financial scenarios for the subsequent years. To successfully build the model we use R Programming. Results and Conclusion: The model predicts future values of income, expenditure and using these two, the profit or loss scenarios can be used for forecasting from year 2018 to 2025. The results forecast that Income would increase or decrease in contest of the Expenditure. As a result Biman Bangladesh may have face significant losses in the years 2020, 2021 and 2024.

Intervention Modeling

10.1093/oso/9780190661557.003.0005 ◽

2017 ◽

Author(s):

Richard McCleary ◽

David McDowall ◽

Bradley J. Bartos

Keyword(s):

Time Series ◽

Moving Average ◽

Intervention Model ◽

Noise Component ◽

Noise Function ◽

Modeling Strategy ◽

The Impact ◽

True Structure

The general AutoRegressive Integrated Moving Average (ARIMA) model can be written as the sum of noise and exogenous components. If an exogenous impact is trivially small, the noise component can be identified with the conventional modeling strategy. If the impact is nontrivial or unknown, the sample AutoCorrelation Function (ACF) will be distorted in unknown ways. Although this problem can be solved most simply when the outcome of interest time series is long and well-behaved, these time series are unfortunately uncommon. The preferred alternative requires that the structure of the intervention is known, allowing the noise function to be identified from the residualized time series. Although few substantive theories specify the “true” structure of the intervention, most specify the dichotomous onset and duration of an impact. Chapter 5 describes this strategy for building an ARIMA intervention model and demonstrates its application to example interventions with abrupt and permanent, gradually accruing, gradually decaying, and complex impacts.