scholarly journals Forecasting Rice Productivity and Production of Odisha, India, Using Autoregressive Integrated Moving Average Models

2014 ◽  
Vol 2014 ◽  
pp. 1-9
Author(s):  
Rahul Tripathi ◽  
A. K. Nayak ◽  
R. Raja ◽  
Mohammad Shahid ◽  
Anjani Kumar ◽  
...  
Keyword(s):  
Autocorrelation Function ◽  
Moving Average ◽  
Selection Criterion ◽  
Information Criteria ◽  
Arima Models ◽  
Autoregressive Integrated Moving Average ◽  
Rice Productivity ◽  
Rice Area ◽  
Moving Average Models ◽  
Partial Autocorrelation Function

Forecasting of rice area, production, and productivity of Odisha was made from the historical data of 1950-51 to 2008-09 by using univariate autoregressive integrated moving average (ARIMA) models and was compared with the forecasted all Indian data. The autoregressive (p) and moving average (q) parameters were identified based on the significant spikes in the plots of partial autocorrelation function (PACF) and autocorrelation function (ACF) of the different time series. ARIMA (2, 1, 0) model was found suitable for all Indian rice productivity and production, whereas ARIMA (1, 1, 1) was best fitted for forecasting of rice productivity and production in Odisha. Prediction was made for the immediate next three years, that is, 2007-08, 2008-09, and 2009-10, using the best fitted ARIMA models based on minimum value of the selection criterion, that is, Akaike information criteria (AIC) and Schwarz-Bayesian information criteria (SBC). The performances of models were validated by comparing with percentage deviation from the actual values and mean absolute percent error (MAPE), which was found to be 0.61 and 2.99% for the area under rice in Odisha and India, respectively. Similarly for prediction of rice production and productivity in Odisha and India, the MAPE was found to be less than 6%.

scholarly journals ANALISIS TIME SERIES DENGAN METODE ARIMA DAN APLIKASINYA

2020 ◽  
Vol 1 (2) ◽  
pp. 26-36
Author(s):  
Fathorrozi Ariyanto ◽  
Moh. Badri Tamam
Keyword(s):  
Time Series ◽  
Autocorrelation Function ◽  
Moving Average ◽  
Autoregressive Integrated Moving Average ◽  
Partial Autocorrelation Function ◽  
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 : 

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.

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

2012 ◽  
Author(s):  
Ayob Katimon ◽  
Amat Sairin Demun
Keyword(s):  
Time Series ◽  
Water Use ◽  
Autocorrelation Function ◽  
Water Consumption ◽  
Moving Average ◽  
Arima Model ◽  
Information Criterion ◽  
University Campus ◽  
Autoregressive Integrated Moving Average ◽  
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

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

2021 ◽  
pp. 1-6
Author(s):  
S. Agboola ◽  
P. Niyang ◽  
O. Olawepo ◽  
W. Ukponu ◽  
S. Niyang ◽  
...  
Keyword(s):  
Autocorrelation Function ◽  
Moving Average ◽  
Arima Model ◽  
Total Size ◽  
Autoregressive Integrated Moving Average ◽  
Daily Data ◽  
Partial Autocorrelation Function ◽  
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.

scholarly journals Forecasting of Climatic Variables in Dinajpur of Bangladesh

2018 ◽  
Vol 10 (2) ◽  
pp. 163-170
Author(s):  
JA Syeda
Keyword(s):  
Crop Production ◽  
Moving Average ◽  
Climatic Variables ◽  
Mean Square ◽  
Arima Models ◽  
Autoregressive Integrated Moving Average ◽  
Adverse Impacts ◽  
Moving Average Models ◽  
Modeling Techniques ◽  
Forecasting Errors

An attempt was made to forecast the 17 monthly climatic variables for 2005-2012 of Dinajpur using the univariate Box-Jenkin’s ARIMA (autoregressive integrated moving average) modeling techniques for 1948-2004. The 8 years data for 1973-1980 were missing and those data were replaced with the 4 years monthly forecasted data for 1948-1972 and 1981-2004 (reversing the years). The well fitted ARIMA (autoregressive integrated moving average) models were selected from the possible 16 ARIMA models based on the minimum root mean square forecasting errors (RMSFE) with the last 24 observations for all the cases and the residuals followed stationarity and normality. Several outliers were detected in the data which were replaced by the forecasted value. The fitted model for sunshine data (1989-2004) was found ARIMA (1, 1, 1)(1, 1, 1)12 and for evaporation data (1987-2000) was ARIMA (1, 1, 2)(1, 1, 1)12. . The findings supports that the changing term of the climatic variables may have adverse impacts on the crop production in this country.J. Environ. Sci. & Natural Resources, 10(2): 163-170 2017

scholarly journals ARIMA model for forecasting of evaporation of Solapur station of Maharashtra, India

MAUSAM ◽  
2021 ◽  
Vol 63 (4) ◽  
pp. 573-580
Author(s):  
D.T. MESHRAM ◽  
S.D. GORANTIWAR ◽  
A.S. LOHAKARE
Keyword(s):  
Maximum Likelihood ◽  
Autocorrelation Function ◽  
Maximum Likelihood Method ◽  
Arima Model ◽  
Information Criteria ◽  
Likelihood Method ◽  
Model Parameters ◽  
Arima Models ◽  
Partial Autocorrelation Function ◽  
Semi Arid

This paper deals with the stochastic modeling of weekly evaporation by using Seasonal ARIMA model for weekly evaporation data for the period of 1987-2008 with a total of 1144 readings for semi-arid Solapur station in Maharashtra. ARIMA models of 1st order were selected based on observing autocorrelation function (ACF) and partial autocorrelation function (PACF) of the weekly evaporation series. The model parameters were obtained by using maximum likelihood method with the help of three tests (i.e., standard error, ACF and PACF of residuals and Akaike Information Criteria). Adequacy of the selected models was determined. The ARIMA model that passed the adequacy test was selected for forecasting. The Seasonal ARIMA (1, 0, 1) (1, 0, 1)52 with lower RMSE is finally selected for forecasting of weekly evaporation values, at Solapur.

scholarly journals Forecasting of Rabi Pulse Production in Odisha (India) by Using Autoregressive Integrated Moving Average (ARIMA) Technique

2020 ◽  
pp. 15-24
Author(s):  
Abhiram Dash ◽  
A. Mangaraju ◽  
Suman . ◽  
Pradeep Mishra
Keyword(s):  
Autocorrelation Function ◽  
Model Building ◽  
Moving Average ◽  
Arima Model ◽  
Original Data ◽  
Percentage Error ◽  
Arima Models ◽  
Autoregressive Integrated Moving Average ◽  
Fit Statistics ◽  
Testing Set

The present study was carried out to forecast the production of rabi pulse in Odisha by using the forecast values of area and yield of rabi pulses obtained from the selected best fit Autoregressive Integrated Moving Average (ARIMA) model. The data from 1971-72 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 and/or the differenced data. The different ARIMA models are judged 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. The best fitted models are selected on the basis of residual diagnostics test and model fit statistics. The ARIMA model found to be best fitted for area under rabi pulse and yield of rabi pulse are ARIMA (2,0,0) with constant and ARIMA (0,1,1) without constant respectively which are successfully cross-validated with the testing set data. The excellent fit ARIMA model has been used to forecast the area and yield of rabi pulse for the years 2016-17, 2017-18 and 2018-19. The forecast value of area shows an increase, where as, the forecast values of yield shows a decrease. The forecast values of production of rabi pulse obtained from the forecast values of area and yield of rabi pulse shows an increase which is due to the increase in forecast value of area. Thus emphasis must be laid on increasing the future yield of rabi pulse so as to achieve sufficient increase in production of rabi pulses which could ensure nutritional security to more extent.

Intervention Modeling

2017 ◽  
Author(s):  
Richard McCleary ◽  
David McDowall ◽  
Bradley J. Bartos
Keyword(s):  
Time Series ◽  
Autocorrelation Function ◽  
Moving Average ◽  
Intervention Model ◽  
Noise Component ◽  
Autoregressive Integrated Moving Average ◽  
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.

scholarly journals Indian COVID-19 dynamics: Prediction using autoregressive integrated moving average modelling

2021 ◽  
Vol 52 (1) ◽  
pp. 6-14
Author(s):  
Amit Tak ◽  
Sunita Dia ◽  
Mahendra Dia ◽  
Todd Wehner
Keyword(s):  
Root Mean Square Error ◽  
Real Time ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Moving Average ◽  
Information Criteria ◽  
Evidence Based ◽  
Mean Square ◽  
Autoregressive Integrated Moving Average ◽  
Akaike Information Criteria

Background: The forecasting of Coronavirus Disease-19 (COVID-19) dynamics is a centrepiece in evidence-based disease management. Numerous approaches that use mathematical modelling have been used to predict the outcome of the pandemic, including data-driven models, empirical and hybrid models. This study was aimed at prediction of COVID-19 evolution in India using a model based on autoregressive integrated moving average (ARIMA). Material and Methods: Real-time Indian data of cumulative cases and deaths of COVID-19 was retrieved from the Johns Hopkins dashboard. The dataset from 11 March 2020 to 25 June 2020 (n = 107 time points) was used to fit the autoregressive integrated moving average model. The model with minimum Akaike Information Criteria was used for forecasting. The predicted root mean square error (PredRMSE) and base root mean square error (BaseRMSE) were used to validate the model. Results: The ARIMA (1,3,2) and ARIMA (3,3,1) model fit best for cumulative cases and deaths, respectively, with minimum Akaike Information Criteria. The prediction of cumulative cases and deaths for next 10 days from 26 June 2020 to 5 July 2020 showed a trend toward continuous increment. The PredRMSE and BaseRMSE of ARIMA (1,3,2) model were 21,137 and 166,330, respectively. Similarly, PredRMSE and BaseRMSE of ARIMA (3,3,1) model were 668.7 and 5,431, respectively. Conclusion: It is proposed that data on COVID-19 be collected continuously, and that forecasting continue in real time. The COVID-19 forecast assist government in resource optimisation and evidence-based decision making for a subsequent state of affairs.

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.

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