scholarly journals Global Warming in Cameron Highlands: Forecasting its Temperature Level via ARIMA vs ARAR

2021 ◽  
Vol 2084 (1) ◽  
pp. 012009
Author(s):  
Nurzawanah Raihah Zamri ◽  
Nurul Nisa’ Khairol Azmi
Keyword(s):  
Global Warming ◽  
Moving Average ◽  
Sarima Model ◽  
Autoregressive Integrated Moving Average ◽  
Average Temperature ◽  
Monthly Temperature ◽  
Average Global Temperature ◽  
Cameron Highlands ◽  
The Impact ◽  
Research Analysis

Abstract The average global temperature has increased at a rapid rate over the past 50 years leading to global warming. The impact of climate change can be felt across the continents. In this research, analysis was conducted to model and forecast the monthly temperature of Cameron Highlands in 2020 and 2021, against its historical monthly average temperature from January 1990 until December 2019. Two (2) methods namely (i) Seasonal Autoregressive Integrated Moving Average (SARIMA) model and (ii) Autoregressive Autoregressive (ARAR) algorithm were compared to determine the best model to forecast the monthly temperature of Cameron Highlands. SARIMA (1,1,2)(1,1,1)12 was found to be the best at forecasting the monthly temperature in Cameron Highlands as RMSE and MAPE values were lower than ARAR. In year 2021, the temperature in Cameron Highlands is estimated to increase by 1.6 °C. The result of the forecast showed that its monthly temperature was expected to increase in the next two (2) years. Hence, this calls for serious action to be taken by higher authorities.

Estimating the Impact of Non-pharmaceutical Interventions Against COVID-19 on Mumps Incidence in Sichuan, China

2021 ◽  
Author(s):  
Wenqiang Zhang ◽  
Rongsheng Luan
Keyword(s):  
Public Health ◽  
Respiratory Virus ◽  
Moving Average ◽  
Sarima Model ◽  
Potential Reduction ◽  
Autoregressive Integrated Moving Average ◽  
Health Measures ◽  
Virus Incidence ◽  
The Impact ◽  
Pharmaceutical Interventions

Abstract Background: A series of social and public health measures have been implemented to contain coronavirus disease 2019 (COVID-19) in China. We examined the impact of non-pharmaceutical interventions against COVID-19 on mumps incidence as an agent to determine the potential reduction in other respiratory virus incidence.Methods: We modelled mumps incidence per month in Sichuan using a seasonal autoregressive integrated moving average (SARIMA) model, based on the reported number of mumps cases per month from 2017-2020. Results: The epidemic peak of mumps in 2020 is lower than in the preceding years. Whenever compared with the projected cases or the average from corresponding periods in the preceding years (2017-2019), the reported cases in 2020 markedly declined (P<0.001). From January to December, the number of mumps cases was estimated to decrease by 36.3% (33.9% - 38.8%), 34.3% (31.1% - 37.8%), 68.9% (66.1% - 71.6%), 76.0% (73.9% - 77.9%), 67.0% (65.0% - 69.0%), 59.6% (57.6% - 61.6%), 61.1% (58.8% - 63.3%), 49.2% (46.4% - 52.1%), 24.4% (22.1% - 26.8%), 30.0% (27.5% - 32.6%), 42.1% (39.6% - 44.7%), 63.5% (61.2% - 65.8%), respectively. The total number of mumps cases in 2020 was estimated to decrease by 53.6% (52.9% - 54.3%).Conclusion: Our study shows that non-pharmaceutical interventions against COVID-19 have had an effective impact on mumps incidence in Sichuan, China.

scholarly journals Reduced Dengue Incidence During COVID-19 Restrictions in Sri Lanka From March 2020 to April 2021

2021 ◽  
Author(s):  
Sinnathamby Noble Surendran ◽  
Ratnarajah Nagulan ◽  
Kokila Sivabalakrishnan ◽  
Sivasingham Arthiyan ◽  
Annathurai Tharshan ◽  
...  
Keyword(s):  
Public Health ◽  
Sri Lanka ◽  
Moving Average ◽  
Health Concern ◽  
Tropical Islands ◽  
Sarima Model ◽  
Autoregressive Integrated Moving Average ◽  
The Public ◽  
Health Measures ◽  
The Impact

Abstract BackgroundDengue is a major public health concern in Sri Lanka. COVID-19 in Sri Lanka was first detected in January 2020, and has continued to be prevalent in the country since that time. The impact of public health measures imposed to restrict COVID-19 transmission on the incidence of dengue throughout the island and particularly its northern Jaffna district in the period March 2020 to April 2021 was determined.MethodsThe incidence of dengue and COVID-19, rainfall and the public health measures implemented to contain COVID-19 transmission for each district in Sri Lanka were obtained from Government sources. The Seasonal Autoregressive Integrated Moving Average (SARIMA) model was used to predict the dengue incidence expected in March 2020 to April 2021, based on pre-pandemic data and this was compared with the actual reported incidence of dengue during the period of COVID-19 restrictions. Ovitrap collections of Aedes larvae were also carried out in the Gurunagar ward of Jaffna city in the Jaffna district during the 2020 and 2021 lockdown and the findings compared with data from 2019.ResultsThe reported number of dengue cases for the whole country from March 2020 to April 2021 was significantly lower than the numbers of dengue cases predicted from the five years immediately preceding the COVID-19 pandemic (2015-2019). Decreased numbers of dengue cases were reported compared to predicted numbers of cases in all 25 administrative districts in the country including the Jaffna district. Aedes larval numbers collected from ovitraps in the Gurunagar ward in Jaffna city during the COVID-19 lockdown period were decreased, with significantly lower proportions of Ae. aegypti than Ae. albopictus, compared with 2019. ConclusionPublic health measures that restricted movement of people, closed schools, universities and offices in order to contain COVID-19 transmission unexpectedly led to a marked reduction in the incidence of dengue in Sri Lanka, in contrast to Singapore. The differences between the two tropical islands have significant implications for the epidemiology of dengue.

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 Implications of the COVID-19 Lockdown on Dengue Transmission in Malaysia

2021 ◽  
Vol 13 (1) ◽  
pp. 148-160
Author(s):  
Song-Quan Ong ◽  
Hamdan Ahmad ◽  
Ahmad Mohiddin Mohd Ngesom
Keyword(s):  
Large Scale ◽  
Moving Average ◽  
Human Movement ◽  
Population Movement ◽  
Sarima Model ◽  
Autoregressive Integrated Moving Average ◽  
Predominant Species ◽  
Lower Confidence ◽  
Dengue Transmission ◽  
Stage 1

We aim to investigate the effect of large-scale human movement restrictions during the COVID-19 lockdown on both the dengue transmission and vector occurrences. This study compared the weekly dengue incidences during the period of lockdown to the previous years (2015 to 2019) and a Seasonal Autoregressive Integrated Moving Average (SARIMA) model that expected no movement restrictions. We found that the trend of dengue incidence during the first two weeks (stage 1) of lockdown decreased significantly with the incidences lower than the lower confidence level (LCL) of SARIMA. By comparing the magnitude of the gradient of decrease, the trend is 319% steeper than the trend observed in previous years and 650% steeper than the simulated model, indicating that the control of population movement did reduce dengue transmission. However, starting from stage 2 of lockdown, the dengue incidences demonstrated an elevation and earlier rebound by four weeks and grew with an exponential pattern. We revealed that Aedes albopictus is the predominant species and demonstrated a strong correlation with the locally reported dengue incidences, and therefore we proposed the possible diffusive effect of the vector that led to a higher acceleration of incidence rate.

scholarly journals Estimating Causal Effects When the Treatment Affects All Subjects Simultaneously: An Application

2021 ◽  
Vol 5 (2) ◽  
pp. 22
Author(s):  
Chiara Binelli
Keyword(s):  
Machine Learning ◽  
Carbon Dioxide ◽  
Global Warming ◽  
Carbon Dioxide Emissions ◽  
Control Group ◽  
Sources Of Information ◽  
Average Global Temperature ◽  
Causal Impact ◽  
The Impact ◽  
Robust Evidence

Several important questions cannot be answered with the standard toolkit of causal inference since all subjects are treated for a given period and thus there is no control group. One example of this type of questions is the impact of carbon dioxide emissions on global warming. In this paper, we address this question using a machine learning method, which allows estimating causal impacts in settings when a randomized experiment is not feasible. We discuss the conditions under which this method can identify a causal impact, and we find that carbon dioxide emissions are responsible for an increase in average global temperature of about 0.3 degrees Celsius between 1961 and 2011. We offer two main contributions. First, we provide one additional application of Machine Learning to answer causal questions of policy relevance. Second, by applying a methodology that relies on few directly testable assumptions and is easy to replicate, we provide robust evidence of the man-made nature of global warming, which could reduce incentives to turn to biased sources of information that fuels climate change skepticism.

scholarly journals Time-series modelling and forecasting of hand, foot and mouth disease cases in China from 2008 to 2018

2019 ◽  
Vol 147 ◽  
Author(s):  
C. W. Tian ◽  
H. Wang ◽  
X. M. Luo
Keyword(s):  
Time Series ◽  
Seasonal Variation ◽  
Moving Average ◽  
Foot And Mouth Disease ◽  
Mouth Disease ◽  
Sarima Model ◽  
Autoregressive Integrated Moving Average ◽  
Annual Peak ◽  
Time Series Modelling ◽  
Foot And Mouth

AbstractSeasonal autoregressive-integrated moving average (SARIMA) has been widely used to model and forecast incidence of infectious diseases in time-series analysis. This study aimed to model and forecast monthly cases of hand, foot and mouth disease (HFMD) in China. Monthly incidence HFMD cases in China from May 2008 to August 2018 were analysed with the SARIMA model. A seasonal variation of HFMD incidence was found from May 2008 to August 2018 in China, with a predominant peak from April to July and a trough from January to March. In addition, the annual peak occurred periodically with a large annual peak followed by a relatively small annual peak. A SARIMA model of SARIMA (1, 1, 2) (0, 1, 1)12 was identified, and the mean error rate and determination coefficient were 16.86% and 94.27%, respectively. There was an annual periodicity and seasonal variation of HFMD incidence in China, which could be predicted well by a SARIMA (1, 1, 2) (0, 1, 1)12 model.

Application of SARIMAX Model to Forecast Daily Sales in Food Retail Industry

2016 ◽  
Vol 7 (2) ◽  
pp. 1-21 ◽  
Author(s):  
Nari Sivanandam Arunraj ◽  
Diane Ahrens ◽  
Michael Fernandes
Keyword(s):  
Influencing Factors ◽  
Performance Measures ◽  
Moving Average ◽  
Food Product ◽  
Retail Store ◽  
Food Supply Chain ◽  
Sarima Model ◽  
Autoregressive Integrated Moving Average ◽  
Fresh Food ◽  
External Variables

During retail stage of food supply chain (FSC), food waste and stock-outs occur mainly due to inaccurate sales forecasting which leads to inappropriate ordering of products. The daily demand for a fresh food product is affected by external factors, such as seasonality, price reductions and holidays. In order to overcome this complexity and inaccuracy, the sales forecasting should try to consider all the possible demand influencing factors. The objective of this study is to develop a Seasonal Autoregressive Integrated Moving Average with external variables (SARIMAX) model which tries to account all the effects due to the demand influencing factors, to forecast the daily sales of perishable foods in a retail store. With respect to performance measures, it is found that the proposed SARIMAX model improves the traditional Seasonal Autoregressive Integrated Moving Average (SARIMA) model.

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 Community Adaptation Model of Food Security Due to Global Warming in Kulon Progo

2017 ◽  
Author(s):  
Sri Rum Giyarsih
Keyword(s):  
Climate Change ◽  
Global Warming ◽  
Food Security ◽  
Survey Methods ◽  
Secondary Data ◽  
Primary Data ◽  
Structured Interviews ◽  
Intergovernmental Panel ◽  
Average Temperature ◽  
The Impact

Global warming is the increase in the average temperature of the Earth’s surface. According to the IPCC (Intergovernmental Panel on Climate Change) average temperature of the Earth’s surface was global warming is the increase in the average temperature of the 0.74 ± 0.18 0C (1.33 ± 0.32 F) over the last hundred years. The impact of rising temperatures is the climate change effect on agricultural production. If the community does not craft made adaptation to global warming will have an impact on food security. This research aims to know the society’s adaptation to food security as a result of global warming and to know the influence of global warming on food security. The research was carried out based on survey methods. The influence of global warming on food security is identified with a share of household food expenditure and the identification of rainfall. Sampling was done by random sampling. The Data used are the primary and secondary data. Primary Data obtained through structured interviews and depth interview using a questionnaire while the secondary data retrieved from publication data of the Central Bureau Statistics B(BPS), Department of Agriculture and Climatology Meteorology and Geophysics (BMKG). The expected results of the study is to know variations of food security due to global warming in Kulon Progo Regency. Comprehensive knowledge through community participation and related Government increased food security that is used as the basis for drafting the model society’s adaptation to the impacts of global warming.

scholarly journals Effectiveness and Forecasting of Interest Rate Reversal BI 7-Day Repo Rate in Indonesia: Lower Bound on Monetary Policy?

2018 ◽  
Vol 9 (1) ◽  
pp. 171-180
Author(s):  
I Gede Sanica ◽  
I Ketut Nurcita ◽  
I Made Mastra ◽  
Desak Made Sukarnasih
Keyword(s):  
Monetary Policy ◽  
Interest Rate ◽  
Time Series Data ◽  
Moving Average ◽  
Series Data ◽  
Autoregressive Integrated Moving Average ◽  
Implementation Of Monetary Policy ◽  
Repo Rate ◽  
The Right ◽  
The Impact

AbstractThis study aims to analyze effectivity and forecast of interest rate BI 7-Day Repo Rate as policy reference in the implementation of monetary policy. The method was used in this study contains Vector Autoregression (VAR) to estimate effectivity of BI 7-Day Repo Rate and Autoregressive Integrated Moving Average (ARIMA) to forecast of BI 7-Day Repo Rate. Period of observation in this study used time series data during 2016.4 until 2017.6. The result of this research shows that the transformation of the BI Rate to BI 7-Day Repo Rate is the right step in the monetary policy operation in the effort to reach deepening of the financial market and strengthen the interbank money market structure so that it will decrease loan interest rate and encourage credit growth. The effectiveness of the use of BI 7 Day-Repo Rate on price stability is indicated by the positive relationship between the benchmark interest rate and inflation compared to the BI Rate. The impact of BI 7-Day Repo Rate on economic growth that tends to be positive. Forecasting the use of BI 7-Day Repo Rate shows good results with declining value levels, so this will encourage deepening the financial markets.

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