scholarly journals Forecasting of New Cases of COVID-19 in Nigeria Using Autoregressive Fractionally Integrated Moving Average Models

2020 ◽  
pp. 135-146
Author(s):  
Olumide Sunday Adesina ◽  
Samson Adeniyi Onanaye ◽  
Dorcas Okewole ◽  
Amanze C. Egere
Keyword(s):  
Moving Average ◽  
Arima Models ◽  
Rate Of Spread ◽  
Arfima Models ◽  
Global Pandemic ◽  
Proposed Model ◽  
Moving Average Models ◽  
The World ◽  
Arfima Model

The emergence of global pandemic known as COVID-19 has impacted significantly on human lives and measures have been taken by government all over the world to minimize the rate of spread of the virus, one of which is by enforcing lockdown. In this study, Autoregressive fractionally integrated moving average (ARFIMA) Models was used to model and forecast what the daily new cases of COVID-19 would have been ten days after the lockdown was eased in Nigeria and compare to the actual new cases for the period when the lockdown was eased.  The proposed model ARFIMA model was compared with ARIMA (1, 0, 0), and ARIMA (1, 0, 1) and found to outperform the classical ARIMA models based on AIC and BIC values. The results show that the rate of spread of COVID-19 would have been significantly less if the strict lockdown had continued. ARFIMA model was further used to model what new cases of COVID-19 would be ten days ahead starting from 31st of August 2020. Therefore, this study recommends that government should further enforce measures to reduce the spread of the virus if business must continue as usual.

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 The Comparison between ARIMA and ARFIMA Model to Forecast Kijang Emas (Gold) Prices in Malaysia using MAE, RMSE and MAPE

2021 ◽  
Vol 6 (3) ◽  
pp. 22-33
Author(s):  
Atiqa Nur Azza Mahmad Azan ◽  
Nur Faizatul Auni Mohd Zulkifly Mototo ◽  
Pauline Jin Wee Mah
Keyword(s):  
Time Series ◽  
Arima Model ◽  
Time Series Models ◽  
Monthly Average ◽  
Arfima Models ◽  
Gold Market ◽  
Univariate Time Series ◽  
The World ◽  
Arfima Model ◽  
Best Fit

Gold is known as the most valuable commodity in the world because it is a universal currency recognized by every single bank across the globe. Thus, many people were interested in investing gold since gold market was always steadier compared to other investment (Khamis and Awang, 2020). However, the credibility of gold was questionable due to the changes in gold prices caused by a variety of circumstances (Henriksen, 2018). Hence, information on the inflation of gold prices were needed to understand the trend in order to plan for the future in accordance with international gold price standards. The aim of this study was to identify the trend of Kijang Emas monthly average prices in Malaysia from the year 2010 to 2021, to determine the best fit time series model for Kijang Emas prices in Malaysia and using univariate time series models to forecast Kijang Emas prices in Malaysia. The ARIMA and ARFIMA models were used in this study to model and forecast the prices of gold (Kijang Emas) in Malaysia. Each of the actual monthly Kijang Emas prices for 2021 were found to be within the 95% predicted intervals for both the ARIMA and ARFIMA models. The performances for each model were checked by considering the values of MAE, RMSE and MAPE. From the findings, all the MAE, RMSE and MAPE values showed that the ARFIMA model emerged as the better model in forecasting the Kijang Emas prices in Malaysia compared to the ARIMA model.

scholarly journals ARIMA Modelling and Forecasting of COVID-19 Daily Confirmed/Death Cases: A Case Study of Nigeria

2021 ◽  
pp. 59-80
Author(s):  
Essi Isaac Didi ◽  
Nwuju Kingdom ◽  
Etuk Ette Harrison
Keyword(s):  
Moving Average ◽  
Health Sector ◽  
Series Data ◽  
Arima Models ◽  
Upward Trend ◽  
Times Series ◽  
Rate Of Spread ◽  
Short Period ◽  
Data Points ◽  
Death Cases

Aims: The aim of this work is to develop suitable ARIMA models which can be sued to forecast daily confirmed/death cases of COVID-19 in Nigeria. This is subject to developing the model, checking them for suitability and carrying out eight months forecast, and making recommendations for the Nigerian Health sector. Study Design:  The study used daily confirmed and death cases of COVID-19 in Nigeria. Methodology: This work covers times series data on the on the daily confirmed/death cases of COVID-19 in Nigeria, obtained from the Nigerian Centre for Disease Control (NDCD) from 21 March 2020 to 5 May 2020, covering a total of 51 data points. This work  is geared towards developing a suitable  Autoregressive Integrated Moving Average (ARIMA) models which can be used to forecast total daily confirmed/death cases of COVID-19 in Nigeria. Two adequate subset ARIMA (2, 2, 1) and AR (1) models for the confirmed/death cases, respectively, is fitted and discussed Results: A forecast of 239 days – from 6th May 2020 to 31 December 2020 was conducted using the fitted models and we observed that the COVID19 data has an upward trend and is best forecasted within a short period. Conclusion: Critical investigation into the rate of spread of COVID-19 pandemic has shown that, that the daily confirmed cases as well as death cases of the disease tends to follow an upward trend. This work aimed at developing a suitable ARIMA models which can be used to fit a most appropriate subsets  to statistically forecast the actual number of confirmed cases as well as death cases of COVID-19 recorded in Nigeria for a period of 8 months.

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 Fishery Landing Forecasting Using Wavelet-Based Autoregressive Integrated Moving Average Models

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Ani Shabri ◽  
Ruhaidah Samsudin
Keyword(s):  
Time Series Data ◽  
Moving Average ◽  
Arima Model ◽  
Peninsular Malaysia ◽  
Series Data ◽  
Proposed Model ◽  
Moving Average Models ◽  
Recorded Data ◽  
The Individual

The accuracy of the wavelet-ARIMA (WA) model in monthly fishery landing forecasting is investigated in the study. In the first part of the study, the discrete wallet transform (DWT) is used to decompose fishery landing time series data. Then ARIMA, as a powerful forecasting tool, is implemented to predict each wavelet transform subseries components independently. Finally, the prediction results of the modeled subseries components are summed to formulate an ensemble forecast for the original fishery landing series. To assess the effectiveness of this model, monthly fishery landing recorded data from East Johor and Pahang states of Peninsular Malaysia have been used as a case study. The result of the study shows that the proposed model was found to provide more accurate fishery landing series forecasts than the individual ARIMA model.

scholarly journals Modeling the incidence and death rates of COVID-19 pandemic in different regions of the world

2020 ◽  
Vol 9 (s1) ◽  
Author(s):  
Ricardo Puziol de Oliveira ◽  
Jorge Alberto Achcar ◽  
Altacílio Aparecido Nunes
Keyword(s):  
The Novel ◽  
Factors Affecting ◽  
Health Authorities ◽  
Global Pandemic ◽  
Counting Data ◽  
Proposed Model ◽  
The World ◽  
Hard Times ◽  
The Face ◽  
Novel Coronavirus

AbstractThis paper reports a broad study using epidemic-related counting data of COVID-19 disease caused by the novel coronavirus (SARS-CoV-2). The considered dataset refers to 119 countries’ daily counts of reported cases and deaths in a fixed period. For the data analysis, it has been adopted a beta regression model assuming different regions of the world where it was possible to discover important economic, health and social factors affecting the behavior of the pandemic in different countries. The Bayesian method was applied to fit the proposed model. Some interesting conclusions were obtained in this study, which could be of great interest to epidemiologists, health authorities, and the general public in the face of the forthcoming hard times of the global pandemic.

scholarly journals Prediction of water consumption by consumer categories: a case study

2020 ◽  
Vol 42 ◽  
pp. e110
Author(s):  
Jorge Alberto Achcar ◽  
Marcos Valerio Araujo ◽  
Claudio Luis Piratelli ◽  
Ricardo Puziol de Oliveira
Keyword(s):  
Water Consumption ◽  
Bayesian Model ◽  
Moving Average ◽  
Random Effect ◽  
Hierarchical Bayesian Analysis ◽  
Advantages And Disadvantages ◽  
Proposed Model ◽  
Consumption Data ◽  
Moving Average Models ◽  
Water Supply Company

This study introduces a new Bayesian model for predicting water consumption in a medium-sized municipality in the State of São Paulo, Brazil. For the study, a stratified random sample of water consumption for consumers in different consumer categories (residential, industrial, public and commercial) is selected for 55 monthly consecutive measurements of water consumption and the proposed model is compared with some usual existing time series models (moving average models and ARIMA models) commonly used in forecasts. The Bayesian model for the consumption data assumes the presence of a random effect that captures the possible dependence between the monthly consumption for the different categories. A hierarchical Bayesian analysis is done using MCMC (Markov Chain Monte Carlo) methods to generate samples of the joint posterior distribution of interest. A detailed discussion of the results obtained is presented, showing the advantages and disadvantages of each model proposed in terms of feasibility for the municipality's water supply company. The results of this study can be generalized to water consumption data for any municipality.

scholarly journals An algorithm for detecting intense anomalous changes in the time dependence of ionospheric parameters

2019 ◽  
Vol 43 (6) ◽  
pp. 1064-1071 ◽  
Author(s):  
N.V. Fetisova
Keyword(s):  
Weather Forecasting ◽  
Moving Average ◽  
Space Weather Forecasting ◽  
Multi Scale ◽  
Combined Application ◽  
Proposed Model ◽  
Moving Average Models ◽  
Critical Frequency Fof2 ◽  
On Line ◽  
Model Components

The paper presents a modified multicomponent model of ionospheric parameter time series. The model describes regular variations and anomalous changes of a multi-scale structure that characterize the occurrence of ionospheric irregularities. Identification of the model components is based on a combined application of the wavelet transform and autoregressive-integrated moving average models. An algorithm for analyzing ionospheric parameters has been developed on the basis of the proposed model. The algorithm allows the intensive ionospheric anomalies characterizing the occurrence of strong ionospheric storms to be detected on-line. Results of the evaluation of the algorithm performance are presented. The evaluation is performed by the example of processing and analyzing hourly and 15-minute data on the ionospheric critical frequency (foF2) during magnetic storms in 2015 – 2017. The performed estimations showed the efficiency of the algorithm and the possibility of its application for space weather forecasting.

scholarly journals Forecasting COVID-19 new cases in Algeria using Autoregressive fractionally integrated moving average Models (ARFIMA)

2020 ◽  
Author(s):  
Belkacem Balah ◽  
Messaoud Djeddou
Keyword(s):  
Long Memory ◽  
Moving Average ◽  
Prediction Method ◽  
Decision Support Tool ◽  
Upward Trend ◽  
Support Tool ◽  
Moving Average Models ◽  
Auto Regressive ◽  
Health Database ◽  
Arfima Model

AbstractIn this research, an ARFIMA model is proposed to forecast new COVID-19 cases in Algeria two weeks ahead. In the present study, public health database from Algeria health ministry has been used to build an ARFIMA model and used to forecast COVID-19 new cases in Algeria until May 11, 2020.BackgroundThe aim of this study is first to find the best prediction method among the two techniques used and type of memory, either short or long, of the model constructed for the daily confirmed cases in Algeria, then make forecasts of the confirmed cases in the fifteen next days.MethodsThis study was conducted based on daily new cases of COVID-19 that were collected from the official website of Algerian Ministry of Health from March 1, 2020 to April 26, 2020. Auto Regressive Integrated Moving Average (ARFIMA) model was used to predict the trend of confirmed cases. The evaluation of the fractional differentiation parameter (d) is carried out using OxMetrics 6 software.ResultsThe ARFIMA model (0, 0.431779, 0) build for Algeria, has a long memory and an upward trend over the next fifteen days and which coincides with the holy month of Ramadhan.ConclusionsThe forecasted results obtained by the proposed ARFIMA model can be used as a decision support tool to manage medical efforts and facilities against the COVID-19 pandemic crisis.

Swarm Optimized Grey SVR and ARIMA for Modeling of Larceny-Theft Rate with Economic Indicators

2017 ◽  
Vol 16 (02) ◽  
pp. 1750008
Author(s):  
Razana Alwee ◽  
Siti Mariyam Shamsuddin ◽  
Roselina Sallehuddin
Keyword(s):  
Nonlinear Models ◽  
Moving Average ◽  
Economic Indicators ◽  
Support Vector ◽  
Features Selection ◽  
Arima Models ◽  
Real World Data ◽  
Proposed Model ◽  
Linear And Nonlinear ◽  
Nonlinear Components

As real world data, larceny-theft rates are most likely to have both linear and nonlinear components. A single model such as the linear or nonlinear model may not be sufficient to model the larceny-theft rate. Thus, a hybridization of the linear and nonlinear models is proposed for modeling the larceny-theft rate. The proposed model combines Support Vector Regression (SVR) and Autoregressive Integrated Moving Average (ARIMA) models. Particle swarm optimization is used to optimize the parameters of SVR and ARIMA models. The proposed model is equipped with features selection that combines grey relational analysis and SVR to choose the significant economic indicators for the larceny-theft rate. The experimental results show that the proposed model has better accuracy than the linear, nonlinear, and existing hybrid models in modeling the larceny-theft rate of United States.

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