scholarly journals Forecasting daily confirmed COVID-19 cases in Algeria using ARIMA models

2020 ◽  
Author(s):  
Messis Abdelaziz ◽  
Adjebli Ahmed ◽  
Ayeche Riad ◽  
Ghidouche Abderrezak ◽  
Ait-Ali Djida
Keyword(s):  
Moving Average ◽  
Arima Model ◽  
Arima Models ◽  
Forecast Period ◽  
Autoregressive Integrated Moving Average ◽  
Hospital Reform ◽  
Estimated Parameters ◽  
Double Exponential ◽  
Double Exponential Smoothing ◽  
Exponential Smoothing Method

ABSTRACTCoronavirus disease has become a worldwide threat affecting almost every country in the world. The aim of this study is to identify the COVID-19 cases (positive, recovery and death) in Algeria using the Double Exponential Smoothing Method and an Autoregressive Integrated Moving Average (ARIMA) model for forecasting the COVID-19 cases.The data for this study were obtained from March 21st, 2020 to November 26th, 2020. The daily Algerian COVID-19 confirmed cases were sourced from The Ministry of Health, Population and Hospital Reform of Algeria. Based on the results of PACF, ACF, and estimated parameters of the ARIMA model in the COVID-19 case in Algeria following the ARIMA model (0,1,1). Observed cases during the forecast period were accurately predicted and were placed within the prediction intervals generated by the fitted model. This study shows that ARIMA models with optimally selected covariates are useful tools for monitoring and predicting trends of COVID-19 cases in Algeria.

scholarly journals Identification COVID-19 Cases in Indonesia with The Double Exponential Smoothing Method

2020 ◽  
Vol 6 (1) ◽  
pp. 66-75 ◽  
Author(s):  
Sri Harini
Keyword(s):  
Arima Model ◽  
Exponential Smoothing ◽  
Smoothing Method ◽  
Estimated Parameters ◽  
Double Exponential ◽  
Time Series Approach ◽  
Smoothing Parameters ◽  
Double Exponential Smoothing ◽  
National Disaster ◽  
Exponential Smoothing Method

The time-series approach is a method used to analyze a series of data in a time sequence to estimate the value of a series in the future. This article will identification the COVID-19 case model in Indonesia using the Double Exponential Smoothing Method. The Double Exponential Smoothing method is one method that can be used to optimize the estimation of the ARIMA model with smoothing parameters α. The data used is sourced from the National Disaster Management Agency which was released starting March 2, 2020. Based on the results of PACF, ACF, and estimated parameters of the ARIMA model in the Covid-19 case in Indonesia following the ARIMA model (0,1,1).

scholarly journals Estimating COVID-19 cases in Makkah region of Saudi Arabia: Space-time ARIMA modeling

PLoS ONE ◽  
2021 ◽  
Vol 16 (4) ◽  
pp. e0250149
Author(s):  
Fuad A. Awwad ◽  
Moataz A. Mohamoud ◽  
Mohamed R. Abonazel
Keyword(s):  
Saudi Arabia ◽  
Moving Average ◽  
Arima Model ◽  
Daily Basis ◽  
World Health ◽  
Arima Models ◽  
Autoregressive Integrated Moving Average ◽  
First Case ◽  
Novel Coronavirus ◽  
The Impact

The novel coronavirus COVID-19 is spreading across the globe. By 30 Sep 2020, the World Health Organization (WHO) announced that the number of cases worldwide had reached 34 million with more than one million deaths. The Kingdom of Saudi Arabia (KSA) registered the first case of COVID-19 on 2 Mar 2020. Since then, the number of infections has been increasing gradually on a daily basis. On 20 Sep 2020, the KSA reported 334,605 cases, with 319,154 recoveries and 4,768 deaths. The KSA has taken several measures to control the spread of COVID-19, especially during the Umrah and Hajj events of 1441, including stopping Umrah and performing this year’s Hajj in reduced numbers from within the Kingdom, and imposing a curfew on the cities of the Kingdom from 23 Mar to 28 May 2020. In this article, two statistical models were used to measure the impact of the curfew on the spread of COVID-19 in KSA. The two models are Autoregressive Integrated Moving Average (ARIMA) model and Spatial Time-Autoregressive Integrated Moving Average (STARIMA) model. We used the data obtained from 31 May to 11 October 2020 to assess the model of STARIMA for the COVID-19 confirmation cases in (Makkah, Jeddah, and Taif) in KSA. The results show that STARIMA models are more reliable in forecasting future epidemics of COVID-19 than ARIMA models. We demonstrated the preference of STARIMA models over ARIMA models during the period in which the curfew was lifted.

scholarly journals ARIMA Time Series Models for Full Truckload Transportation Prices

Forecasting ◽  
2018 ◽  
Vol 1 (1) ◽  
pp. 121-134 ◽  
Author(s):  
Jason W. Miller
Keyword(s):  
Time Series ◽  
Moving Average ◽  
The United States ◽  
Arima Models ◽  
Autoregressive Integrated Moving Average ◽  
Estimated Parameters ◽  
Coefficients Of Variation ◽  
Labor Statistics ◽  
Bureau Of Labor Statistics ◽  
Practical Implications

The trucking sector in the United States is a $700 billion plus a year industry and represents a large percentage of many firms’ logistics spend. Consequently, there is interest in accurately forecasting prices for truck transportation. This manuscript utilizes the autoregressive integrated moving average (ARIMA) methodology to develop forecasts for three time series of monthly archival trucking prices obtained from two public sources—the Bureau of Labor Statistics (BLS) and Truckstop.com. BLS data cover January 2005 through August 2018; Truckstop.com data cover January 2015 through August 2018. Different ARIMA models closely approximate the observed data, with coefficients of variation of the root mean-square deviations being 0.007, 0.040, and 0.048. Furthermore, the estimated parameters map well onto dynamics known to operate in the industry, especially for data collected by the BLS. Theoretical and practical implications of these findings are discussed.

scholarly journals Komparasi Metode Peramalan Jumlah Permintaan Kamar Hotel

2019 ◽  
Vol 9 (2) ◽  
Author(s):  
Rendra Gustriansyah ◽  
Wilza Nadia ◽  
Mitha Sofiana
Keyword(s):  
Moving Average ◽  
Exponential Smoothing ◽  
Standard Type ◽  
The Public ◽  
Forecasting Methods ◽  
Forecasting Method ◽  
Double Exponential ◽  
Double Exponential Smoothing ◽  
Exponential Smoothing Method ◽  
Single Exponential

<p class="SammaryHeader" align="center"><strong><em>Abstract</em></strong></p><p><em>Hotel is  a type of accommodation that uses most or all of the buildings to provide lodging, dining and drinking services, and other services for the public, which are managed commercially so that each hotel will strive to optimize its functions in order to obtain maximum profits. One such effort is to have the ability to forecast the number of requests for hotel rooms in the coming period. Therefore, this study aims to forecast the number of requests for hotel rooms in the future by using five forecasting methods, namely linear regression, single moving average, double moving average, single exponential smoothing, and double exponential smoothing, as well as to compare forecasting results with these five methods so that the best forecasting method is obtained. The data used in this study is data on the number of requests for standard type rooms from January to November in 2018, which were obtained from the Bestskip hotel in Palembang. The results showed that the single exponential smoothing method was the best forecasting method for data patterns as in this study because it produced the smallest MAPE value of 41.2%.</em></p><p><strong><em>Keywords</em></strong><em>: forecasting, linier regression, moving average, exponential smoothing.</em></p><p align="center"><strong><em>Abstrak</em></strong></p><p><em>Hotel merupakan jenis akomodasi yang mempergunakan sebagian besar atau seluruh bangunan untuk menyediakan jasa penginapan, makan dan minum serta jasa lainnya bagi umum, yang dikelola secara komersial, sehingga setiap hotel akan berupaya untuk mengoptimalkan fungsinya agar memperoleh keuntungan maksimum. Salah satu upaya tersebut adalah memiliki kemampuan untuk meramalkan jumlah permintaan terhadap kamar hotel pada periode mendatang. Oleh karena itu, penelitian ini bertujuan untuk meramalkan jumlah permintaan terhadap kamar hotel di  masa mendatang dengan menggunakan lima metode peramalan, yaitu regresi linier, single moving average, double moving average, single exponential smoothing, dan double exponential smoothing, serta untuk mengetahui perbandingan hasil peramalan dengan kelima metode tersebut sehingga diperoleh metode peramalan terbaik. Adapun data yang digunakan dalam penelitian ini merupakan data jumlah permintaan kamar tipe standar dari bulan Januari hingga November tahun 2018, yang diperoleh dari hotel Bestskip Palembang. Hasil penelitian menunjukkan bahwa metode single exponential smoothing merupakan metode peramalan terbaik untuk pola data seperti pada penelitian ini karena menghasilkan nilai MAPE paling kecil sebesar 41.2%.</em></p><strong><em>Kata kunci</em></strong><em>: peramalan, regeresi linier, moving average, exponential smoothing.</em>

scholarly journals Using Autoregressive Integrated Moving Average (ARIMA) Technique to Forecast the Production of Kharif Cereals in Odisha (India)

2020 ◽  
pp. 104-113
Author(s):  
Abhiram Dash ◽  
A. Mangaraju ◽  
Pradeep Mishra ◽  
H. Nayak
Keyword(s):  
Autocorrelation Function ◽  
Moving Average ◽  
Arima Model ◽  
Percentage Error ◽  
Arima Models ◽  
Training Set ◽  
Autoregressive Integrated Moving Average ◽  
Future Production ◽  
Best Fit ◽  
Testing Set

Cereals are the most important kharif season crop in Odisha. The present study was carried out to forecast the production of kharif cereals in Odisha by using the forecast values of area and yield of kharif cereals obtained from the selected best fit Autoregressive Integrated Moving Average (ARIMA) model. The data from 1970-71 to 2010-11 are considered as training set data and used for model building and from 2011-12 to 2015-16 are considered as testing set data and used for cross-validation of the selected model on the basis of the absolute percentage error. The ARIMA models are fitted to the stationary data which may be the original data or the differenced data. The different ARIMA models are evaluated on the basis of Autocorrelation Function (ACF) and Partial Autocorrelation Function (PACF) at various lags. The possible ARIMA models are selected on the basis of significant coefficient of autoregressive and moving average components by using the training set data. The best fitted models are then selected on the basis of residual diagnostics test and model fit statistics. The ARIMA model found to be best fitted for area under kharif cereals and yield of kharif cereals are ARIMA (1,1,0) without constant and ARIMA (0,1,2) without constant respectively which are successfully cross-validated with the testing set data. The respective best fit ARIMA model has been used to forecast the area and yield of kharif cereals for the years 2016-17, 2017-18 and 2018-19. The forecast values of area shows a decrease, whereas, the forecast values of yield shows an increase. The decrease in area might have been the result of limited availability of area for cereals due to shifting towards non-food grain crops. The forecast values of production of kharif cereals obtained from the forecast values of area and yield of kharif cereals shows an increase which is due to the increase in forecast values of yield. Since there is limited scope for area expansion, the future production of kharif cereals can only be increased by increasing the yield to achieve the goal of food security for the growing population.

scholarly journals An ARIMA Model to Forecast the Spread and the Final Size of COVID-2019 Epidemic in Italy

2020 ◽  
Author(s):  
Gaetano Perone
Keyword(s):  
Inflection Point ◽  
Social Impact ◽  
Moving Average ◽  
Arima Model ◽  
Epidemiological Data ◽  
List Type ◽  
Arima Models ◽  
Final Size ◽  
Autoregressive Integrated Moving Average ◽  
The World

AbstractCoronavirus disease (COVID-2019) is a severe ongoing novel pandemic that is spreading quickly across the world. Italy, that is widely considered one of the main epicenters of the pandemic, has registered the highest COVID-2019 death rates and death toll in the world, to the present day. In this article I estimate an autoregressive integrated moving average (ARIMA) model to forecast the epidemic trend over the period after April 4, 2020, by using the Italian epidemiological data at national and regional level. The data refer to the number of daily confirmed cases officially registered by the Italian Ministry of Health (www.salute.gov.it) for the period February 20 to April 4, 2020. The main advantage of this model is that it is easy to manage and fit. Moreover, it may give a first understanding of the basic trends, by suggesting the hypothetic epidemic’s inflection point and final size.Highlights❖ARIMA models allow in an easy way to investigate COVID-2019 trends, which are nowadays of huge economic and social impact.❖These data may be used by the health authority to continuously monitor the epidemic and to better allocate the available resources.❖The results suggest that the epidemic spread inflection point, in term of cumulative cases, will be reached at the end of May.❖Further useful and more precise forecasting may be provided by updating these data or applying the model to other regions and countries.

scholarly journals Determine neighboring region spatial effect on dengue cases using ensemble ARIMA models

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Loshini Thiruchelvam ◽  
Sarat Chandra Dass ◽  
Vijanth Sagayan Asirvadam ◽  
Hanita Daud ◽  
Balvinder Singh Gill
Keyword(s):  
Moving Average ◽  
Arima Model ◽  
Information Criterion ◽  
Epidemiological Data ◽  
The State ◽  
Spatial Effects ◽  
Arima Models ◽  
Autoregressive Integrated Moving Average ◽  
Temperature Levels ◽  
Surrounding Areas

AbstractThe state of Selangor, in Malaysia consist of urban and peri-urban centres with good transportation system, and suitable temperature levels with high precipitations and humidity which make the state ideal for high number of dengue cases, annually. This study investigates if districts within the Selangor state do influence each other in determining pattern of dengue cases. Study compares two different models; the Autoregressive Integrated Moving Average (ARIMA) and Ensemble ARIMA models, using the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) measurement to gauge their performance tools. ARIMA model is developed using the epidemiological data of dengue cases, whereas ensemble ARIMA incorporates the neighbouring regions’ dengue models as the exogenous variable (X), into traditional ARIMA model. Ensemble ARIMA models have better model fit compared to the basic ARIMA models by incorporating neighbuoring effects of seven districts which made of state of Selangor. The AIC and BIC values of ensemble ARIMA models to be smaller compared to traditional ARIMA counterpart models. Thus, study concludes that pattern of dengue cases for a district is subject to spatial effects of its neighbouring districts and number of dengue cases in the surrounding areas.

scholarly journals Predicting the throughput of grain products at the multipurpose terminal at the Port of Cape Town

2016 ◽  
Vol 10 (1) ◽  
Author(s):  
Leila L. Goedhals-Gerber
Keyword(s):  
Supply Chains ◽  
Moving Average ◽  
Arima Model ◽  
Cape Town ◽  
Arima Models ◽  
Autoregressive Integrated Moving Average ◽  
Upper Limits ◽  
Grain Products ◽  
And Performance ◽  
And Storage

Background: Ports provide vital links in the maritime supply chains on which the trading of countries depend, and their efficiency and performance can contribute largely to the international competitiveness of those countries. However, to achieve and maintain such a contribution, port operators need to understand their role in a national economy and the factors that underlie the efficiency of the intermodal link that ports constitute in international supply chains. One such factor is the capacity of specialised cargo terminals.Objectives: This article described a possible technique for forecasting the throughput of grain imports through the bulk grain terminal at the Port of Cape Town. It determined whether the capacity in the bulk grain terminal is sufficient to handle current and forecasted volumes of imported grains or whether the volumes justify expansion or upgrading of the bulk grain terminal in the Port of Cape Town.Method: The Box–Jenkins methodology for autoregressive integrated moving average (ARIMA) models was applied. An ARIMA model – 2 parameter, 1 difference – was selected to do the forecast.Results: The average tonnage of all grains imported through the Port of Cape Town that can be expected in a month is approximately 90 000 tons. The maximum tonnage of all grains imported through the Port of Cape Town that can be expected in a month is approximately 180 000 tons.Conclusion: The analyses show that the demand for imports of grain products at the multipurpose terminal in the Port of Cape Town is not growing substantially. The analyses also identify that the current upper limits of grain imports are within the existing handling and storage capacities of the bulk grain terminal.

scholarly journals Forecasting Sugarcane Yield of India based on rough set combination approach

2021 ◽  
Vol 4 (2) ◽  
pp. 163-177
Author(s):  
Haresh Kumar Sharma ◽  
◽  
Kriti Kumari ◽  
Samarjit Kar ◽  
◽  
...  
Keyword(s):  
Rough Set ◽  
Moving Average ◽  
Weight Coefficient ◽  
Grey Model ◽  
Autoregressive Integrated Moving Average ◽  
Sugarcane Production ◽  
Double Exponential ◽  
Sugarcane Yield ◽  
Double Exponential Smoothing ◽  
Combination Approach

This study applied a novel rough set combination approach for forecasting sugarcane production in India. The paper uses autoregressive integrated moving average (ARIMA), double exponential smoothing (DES) and Grey model (GM) to generate the single forecasts. Also, the weight coefficient is evaluated by underlying the rough set approach to combine the single forecasts obtained from different models. To validate our proposed analysis, Sugarcane from 1950 to 2011 was used for the overall empirical analysis and generate out-sample forecasts from 2012 to 2021 for the comparative analysis. Also, ARIMA (2, 1, 1) model is found more appropriate for forecasting Sugarcane production.

scholarly journals PERBANDINGAN METODE AUTOREGRESSIVE INTEGRATED MOVING AVERAGE DAN METODE DOUBLE EXPONENTIAL SMOOTHING DARI HOLT DALAM MERAMALKAN NILAI IMPOR DI INDONESIA

2020 ◽  
Vol 1 (1) ◽  
pp. 37-46
Author(s):  
YULINAR I. AJUNU ◽  
NOVIANITA ACHMAD ◽  
MUHAMMAD REZKY FRIESTA PAYU
Keyword(s):  
Optimal Parameter ◽  
Moving Average ◽  
Model Identification ◽  
Accurate Method ◽  
Exponential Smoothing ◽  
Autoregressive Integrated Moving Average ◽  
Double Exponential ◽  
Double Exponential Smoothing ◽  
Value Determination ◽  
Data Plotting

As a form of purchased goods from other state’s imports have impacts both positive and negative to the states’s condition; therefore, prediction is required. Employing Autoregressive Integrated Moving Average (ARIMA) and Holt’s Double Exponential Smoothing (DES) methods, this study intends to identify which of the methods is the most accurate to predict Indonesia’s import value.  The ARIMA method stage involved: data ploting, data stasioneriation, temporary model identification, parameter estimation, test residual assumption, and prediction. Moreover, the Holt’s DES method involved: data plotting, initial value determination, optimal parameter identification, Level Lt and Trend Tt value quantification, andprediction. The result shows that ARIMA method is the most accurate method to predict Indonesia’s import value.

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