Periodic occurrences of annual rainfalls in Eastern India [UPH No. 9 (theme: Variability of extremes) and UPH No.19 (theme: Modelling methods)]

2020 ◽  
Author(s):  
Dr Subhabrata Panda
Keyword(s):  
Polynomial Regression ◽  
Moving Average ◽  
Arima Model ◽  
Rainfall Series ◽  
Rainfall Data ◽  
Annual Rainfall ◽  
Data Series ◽  
Eastern India ◽  
Autoregressive Integrated Moving Average ◽  
The Mean

<p>Long period annual rainfall data series from nine raingauge stations throughout eastern India were analysed. Those data series were for the years 1901 to 1965 for Aijal (Mizoram); 1901 to 1984 for Imphal (Manipur); 1901 to 1986 for Guwahati (Assam), Shillong, Cherrapunji (Meghalaya); 1901 to 1987 for Cuttack (Odisha), Patna (Bihar), Agartala (Tripura), Krishnanagar (West Bengal). Incomplete annual rainfall data were found out by taking average of data of preceding and following years. Each annual rainfall series was divided into modelled period (1901 to 1980 for eight stations except Aijal with 1901 to 1960) and predicted period (data for years left in the series after modelled period for evaluation of the model for prediction of future rainfalls). Each annual rainfall series in the modelled period was first converted into percentage values of the mean annual rainfall and then plotted against year, which showed the oscillations of the historigram about the mean line (Tomlinson, 1987 for New Zealand rainfalls). Such type of characteristic historigrams for all stations showed periodic nature of annual rainfalls throughout eastern India. So, autoregressive integrated moving average (ARIMA) model (Clarke, 1973) was used to evolve a useful model for prediction of future rainfalls. As the ARIMA model was biased for periodicity due to inclusion of both the ‘sin’ and ‘cos’ functions and period length as 12, modelled data series were analysed for polynomial regression. The accepted degrees of polynomials were decided on the basis of analysis of variance (ANOVA). Acceptance of either ARIMA model or polynomial regression was done on the basis of -test. In most of the cases in the observed historigrams the lengths of periods were less than eight years and in some cases those were eight to 12 years and from polynomial regressions in most cases the period lengths varied in between 8 to 12 years, 13 to 22 years and 23 to 37 years; and in rare cases those lengths were 38 years and more. Considering all the limitations in the observed data and 95% confidence interval for ARIMA model, a particular amount of annual rainfall occurred at about 12 years (i.e. almost resembling a Solar Cycle) and that might be concluded after minute analysis of more observed data. Recurrence of flood and drought years can be predicted from such analysis and also by following probability analysis of excess and deficit runs of annual rainfalls (Panda <em>et al</em>., 1996).</p><p>References:</p><p>Clarke, R.T.1973. Mathematical models in hydrology. FAO Irrigation and Drainage Paper No. 19. FAO of the United Nations, Rome. pp.101-108.</p><p>Panda, S.; Datta, D.K. and Das, M.N. (1996). Prediction of drought and flood years in Eastern India using length of runs of annual rainfall. J. Soil Wat. Conserv. India. 40(3&4):184-191.</p><p>          https://www.academia.edu/15034719/Prediction_of_drought_and_flood_years_in_eastern_%20%09India%20using_length_of_runs_of_annual_rainfall</p><p>Tomlinson, A.I. (1987). Wet and dry years – seven years on. Soil & Water. Winter 1987: 8-9. ISSN 0038-0695    </p>

A Stochastic Model to Analyze and Predict Transmission Dynamics of Tuberculosis in Ede Kingdom of Osun State

2018 ◽  
Vol 7 (1) ◽  
Author(s):  
Kehinde Adekunle Bashiru
Keyword(s):  
Process Model ◽  
Goodness Of Fit ◽  
Moving Average ◽  
Arima Model ◽  
Probability Generating Function ◽  
Information Criteria ◽  
Data Series ◽  
Suitable Model ◽  
Birth Process ◽  
Autoregressive Integrated Moving Average

In this study the stochastic process model for estimating the incidence of tuberculosis (TB) infection in Ede kingdom (Ede North and Ede South Local Government Areas) of Osun State was carried out. The probability generating function approach was used to solve the associated birth process model to obtain the estimate of TB incidence. Also time series analysis was carried out using JMulti software to predict future incidence rate of the disease in the study area. Based on Autoregressive Integrated Moving Average (ARIMA) model, the autocorrelation and partial autocorrelation methods and a suitable model to forecast TB infection was obtained.  The goodness of fit was measured using the Akaike Information Criteria (AIC) and Bayesian Information Criteria (BIC). Having satisfied all the model assumptions ARIMA (0,1,1) model with standard error, 6.37086 was found to be the best model for the forecast. It was observed that the forecasted series were close to the actual data series

scholarly journals Comparing Statistical Models to Predict Dengue Fever Notifications

2012 ◽  
Vol 2012 ◽  
pp. 1-6 ◽  
Author(s):  
Arul Earnest ◽  
Say Beng Tan ◽  
Annelies Wilder-Smith ◽  
David Machin
Keyword(s):  
Dengue Fever ◽  
Statistical Models ◽  
Disease Surveillance ◽  
Moving Average ◽  
Arima Model ◽  
Public Health Problem ◽  
Percentage Error ◽  
Mosquito Vector ◽  
Autoregressive Integrated Moving Average ◽  
The Mean

Dengue fever (DF) is a serious public health problem in many parts of the world, and, in the absence of a vaccine, disease surveillance and mosquito vector eradication are important in controlling the spread of the disease. DF is primarily transmitted by the femaleAedes aegyptimosquito. We compared two statistical models that can be used in the surveillance and forecast of notifiable infectious diseases, namely, the Autoregressive Integrated Moving Average (ARIMA) model and the Knorr-Held two-component (K-H) model. The Mean Absolute Percentage Error (MAPE) was used to compare models. We developed the models using used data on DF notifications in Singapore from January 2001 till December 2006 and then validated the models with data from January 2007 till June 2008. The K-H model resulted in a slightly lower MAPE value of 17.21 as compared to the ARIMA model. We conclude that the models' performances are similar, but we found that the K-H model was relatively more difficult to fit in terms of the specification of the prior parameters and the relatively longer time taken to run the models.

scholarly journals S&P BSE Sensex and S&P BSE IT return forecasting using ARIMA

2020 ◽  
Vol 6 (1) ◽  
Author(s):  
Madhavi Latha Challa ◽  
Venkataramanaiah Malepati ◽  
Siva Nageswara Rao Kolusu
Keyword(s):  
Stock Returns ◽  
Stock Exchange ◽  
Moving Average ◽  
Arima Model ◽  
Descriptive Statistics ◽  
Autoregressive Integrated Moving Average ◽  
Augmented Dickey Fuller ◽  
The Mean ◽  
Daily Stock Returns

AbstractThis study forecasts the return and volatility dynamics of S&P BSE Sensex and S&P BSE IT indices of the Bombay Stock Exchange. To achieve the objectives, the study uses descriptive statistics; tests including variance ratio, Augmented Dickey-Fuller, Phillips-Perron, and Kwiatkowski Phillips Schmidt and Shin; and Autoregressive Integrated Moving Average (ARIMA). The analysis forecasts daily stock returns for the S&P BSE Sensex and S&P BSE IT time series, using the ARIMA model. The results reveal that the mean returns of both indices are positive but near zero. This is indicative of a regressive tendency in the long-term. The forecasted values of S&P BSE Sensex and S&P BSE IT are almost equal to their actual values, with few deviations. Hence, the ARIMA model is capable of predicting medium- or long-term horizons using historical values of S&P BSE Sensex and S&P BSE IT.

scholarly journals Peramalan Harga Bitcoin Menggunakan Metode ARIMA (Autoregressive Integrated Moving Average)

2018 ◽  
Vol 1 (1) ◽  
pp. 21-31
Author(s):  
Nany Salwa ◽  
Nidya Tatsara ◽  
Ridha Amalia ◽  
Aja Fatimah Zohra
Keyword(s):  
Time Series Data ◽  
Moving Average ◽  
Arima Model ◽  
Secondary Data ◽  
Series Data ◽  
Forecasting Model ◽  
Virtual Currency ◽  
Autoregressive Integrated Moving Average ◽  
The Mean ◽  
Level 2

ABSTRAK. Bitcoin merupakan mata uang virtual yang saat ini banyak diminati sebagai alternatif investasi. Metode ARIMA adalah salah satu metode yang digunakan untuk peramalan data deret waktu. Tujuan dari penelitian ini adalah untuk membuat model dan meramalkan harga bitcoin.  Data yang digunakan adalah data sekunder yaitu berupa data harga bitcoin selama 60 periode mulai dari tanggal 10 Januari 2018 sampai dengan 10 Maret 2018 untuk memprediksikan harga bitcoinselama 30 periode kedepan mulai tanggal 11 Maret 2018 sampai dengan 09 April 2018. Dari hasil penelitian menunjukkan bahwa data harga bitcoin selama 60 periode tidak memenuhi asumsi stasioneritas terhadap rata-rata untuk itu dilakukan proses differencing tingkat 2 agar data menjadi stasioner. Model ARIMA yang dihasilkan adalah ARIMA(0,2,1) yaitu  Zt = μ - 0,9647Zt-1 + at dan model tersebut cocok digunakan untuk peramalan data harga bitcoin. Hasil peramalan dengan menggunakan model ARIMA(0,2,1) menunjukkan bahwa harga bitcoin untuk 30 periode kedepannya mengalami penurunan secara perlahan dan hasil peramalan mendekati data sebenarnya. ABSTRACT. Bitcoin is a virtual currency that is currently much interested as an alternative investment. ARIMA method is one of the methods used for forecasting time series data. The purpose of this research is to create a model and predicted the price of the bitcoin.  The data used are secondary data that is in the form of price bitcoin during 60 periods starting from January 10, 2018 up to 10 March 2018 to predict price bitcoin for 30 the next periods began March 11 and ended on 9 April 2018 2018. Based on the results of the study showed that the price of bitcoin during 60 periods did not fullfiled the assumptions of stasioneritas towards the mean. Therefore using the differencing level 2 process, so the data becomes stationary. The result of ARIMA model is ARIMA(0, 2, 1) Zt = μ - 0,9647Zt-1 + at and the model fits the data used for forecasting price bitcoin. The results of the forecasting model using ARIMA (0, 2, 1) shows that the price of the bitcoin for 30 periods has decreased gradually and forecasting results close to the actual data.

scholarly journals A statistical study of COVID-19 pandemic in Egypt

2021 ◽  
Vol 54 (1) ◽  
pp. 233-244
Author(s):  
Taha Radwan
Keyword(s):  
Linear Relationship ◽  
Statistical Study ◽  
Moving Average ◽  
Arima Model ◽  
Decision Makers ◽  
Accurate Data ◽  
Autoregressive Integrated Moving Average ◽  
The World ◽  
The Future

Abstract The spread of the COVID-19 started in Wuhan on December 31, 2019, and a powerful outbreak of the disease occurred there. According to the latest data, more than 165 million cases of COVID-19 infection have been detected in the world (last update May 19, 2021). In this paper, we propose a statistical study of COVID-19 pandemic in Egypt. This study will help us to understand and study the evolution of this pandemic. Moreover, documenting of accurate data and taken policies in Egypt can help other countries to deal with this epidemic, and it will also be useful in the event that other similar viruses emerge in the future. We will apply a widely used model in order to predict the number of COVID-19 cases in the coming period, which is the autoregressive integrated moving average (ARIMA) model. This model depicts the present behaviour of variables through linear relationship with their past values. The expected results will enable us to provide appropriate advice to decision-makers in Egypt on how to deal with this epidemic.

scholarly journals Erosive rainfall in the Rio do Peixe Valley: Part III - Risk of extreme events

2018 ◽  
Vol 22 (1) ◽  
pp. 63-68
Author(s):  
Álvaro J. Back ◽  
Augusto C. Pola ◽  
Nilzo I. Ladwig ◽  
Hugo Schwalm
Keyword(s):  
Extreme Events ◽  
Annual Rainfall ◽  
Data Series ◽  
Rainfall Erosivity ◽  
Significance Level ◽  
Control Structures ◽  
Kolmogorov Smirnov ◽  
Runoff Control ◽  
The Mean ◽  
Erosive Rainfall

ABSTRACT Understanding the risks of extreme events related to soil erosion is important for adequate dimensioning of erosion and runoff control structures. The objective of this study was to determine the rainfall erosivity with different return periods for the Valley of the Rio do Peixe in Santa Catarina state, Brazil. Daily pluviographic data series from 1984 to 2014 from the Campos Novos, and Videira meteorological stations and from 1986 to 2014 from the Caçador station were used. The data series of maximum annual rainfall intensity in 30 min, maximum annual erosive rainfall, and total annual erosivity were analyzed for each station. The Gumbel-Chow distributions were adjusted and their adhesions were evaluated by the Kolmogorov-Smirnov test at a significance level of 5%. The Gumbel-Chow distribution was adequate for the estimation of all studied variables. The mean annual erosivity corresponds to the return period of 2.25 years. The data series of the annual maximum individual rainfall erosivity coefficients varied from 47 to 50%.

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 Autoregressive Integrated Moving Average (ARIMA) Sebagai Model Peramalan Kasus Demam Berdarah Dengue

2020 ◽  
Vol 10 (2) ◽  
pp. 76-80
Author(s):  
Roro Kushartanti ◽  
Maulina Latifah
Keyword(s):  
Community Health ◽  
Health Center ◽  
Moving Average ◽  
Arima Model ◽  
Community Health Center ◽  
Percentage Error ◽  
Dengue Case ◽  
Monthly Data ◽  
Autoregressive Integrated Moving Average ◽  
Forecasting Method

ARIMA is a forecasting method time series that does not require a specific data pattern. This study aims to analyze the forecasting of Semarang City DHF cases specifically in the Rowosari Community Health Center. The study used monthly data on DHF cases in the Rowosari Community Health Center in 2016, 2017, and 2019 as many as 36 dengue case data. The best ARIMA model for forecasting is a model that meets the requirements for parameter significance, white noise and has the MAPE (Mean Absolute Percentage Error Smallest) value. The results of the analysis show that the best model for predicting the number of dengue cases in the Rowosari Public Health Center Semarang is the ARIMA model (1,0,0) with a MAPE value of 43.98% and a significance coefficient of 0.353, meaning that this model is suitable and feasible to be used as a forecasting model. DHF cases in the Rowosari Community Health Center in Semarang City.

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