Hybrid grey exponential smoothing approach for predicting transmission dynamics of the COVID-19 outbreak in Sri Lanka

2022 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
D.M.K.N. Seneviratna ◽  
R.M. Kapila Tharanga Rathnayaka
Keyword(s):  
Time Series ◽  
Sri Lanka ◽  
Exponential Smoothing ◽  
Transmission Dynamics ◽  
Absolute Deviation ◽  
Content Type ◽  
The World ◽  
Critical Issues ◽  
Exponential Trend ◽  
Double Exponential Smoothing

PurposeThe Coronavirus (COVID-19) is one of the major pandemic diseases caused by a newly discovered virus that has been directly affecting the human respiratory system. Because of the gradually increasing magnitude of the COVID-19 pandemic across the world, it has been sparking emergencies and critical issues in the healthcare systems around the world. However, predicting the exact amount of daily reported new COVID cases is the most serious issue faced by governments around the world today. So, the purpose of this current study is to propose a novel hybrid grey exponential smoothing model (HGESM) to predicting transmission dynamics of the COVID-19 outbreak properly.Design/methodology/approachAs a result of the complications relates to the traditional time series approaches, the proposed HGESM model is well defined to handle exponential data patterns in multidisciplinary systems. The proposed methodology consists of two parts as double exponential smoothing and grey exponential smoothing modeling approach respectively. The empirical analysis of this study was carried out on the basis of the 3rd outbreak of Covid-19 cases in Sri Lanka, from 1st March 2021 to 15th June 2021. Out of the total 90 daily observations, the first 85% of daily confirmed cases were used during the training, and the remaining 15% of the sample.FindingsThe new proposed HGESM is highly accurate (less than 10%) with the lowest root mean square error values in one head forecasting. Moreover, mean absolute deviation accuracy testing results confirmed that the new proposed model has given more significant results than other time-series predictions with the limited samples.Originality/valueThe findings suggested that the new proposed HGESM is more suitable and effective for forecasting time series with the exponential trend in a short-term manner.

scholarly journals Forecasting COVID-19 Cases in Indonesia Using Hybrid Double Exponential Smoothing

2021 ◽  
Vol 1 (2) ◽  
pp. 53-57
Author(s):  
Mujiati Dwi Kartikasari
Keyword(s):  
Time Series ◽  
Exponential Smoothing ◽  
Upward Trend ◽  
Double Exponential ◽  
The World ◽  
Trend Data ◽  
Time Series Approach ◽  
Double Exponential Smoothing

The COVID-19 epidemic has spread throughout countries around the world. In Indonesia, this case was detected in early March 2020, and until now, there is still an increase in positive cases of COVID-19. The purpose of this paper is to predict COVID-19 cases in Indonesia using a time series approach. The method used is H-WEMA method because this method can capture trend data patterns following the conditions of COVID-19 cases in Indonesia. Based on the analysis results, H-WEMA can predict COVID-19 cases very well. The forecasted results of the COVID-19 cases in Indonesia still have an upward trend, so it needs the cooperation of all elements of community to reduce the spread of COVID-19. Received September 8, 2021Revised October 15, 2021Accepted November 3, 2021  

A comparison of global suicide terrorism by region before and after 9/11 terrorist attacks

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Ahmet Guler ◽  
Mustafa Demir
Keyword(s):  
Time Series ◽  
Interrupted Time Series ◽  
Sub Saharan Africa ◽  
Terrorist Attacks ◽  
Suicide Terrorism ◽  
Content Type ◽  
The World ◽  
Before And After ◽  
Sub Saharan ◽  
Global Terrorism Database

Purpose This study aims to examine the effect of the 9/11 terrorist attacks on suicide terrorism in different regions of the world and changes in the trends in suicide terrorism according to regions before and after 9/11. Design/methodology/approach Using the data obtained from the Global Terrorism Database from 1981 to 2019, the descriptive statistics were computed first and then, independent samples t-tests were run to compare the monthly mean percentage of suicide-terrorism incidents that occurred in each region between the pre-9/11 and the post-9/11 periods. Finally, to statistically assess the effect of the 9/11 attacks and changes in the trends for the dependent variables over time, monthly interrupted time-series analyzes were conducted. Findings The results of monthly interrupted time series analyzes showed that after the 9/11 attacks, the trends for suicide-terrorism rates decreased significantly in three regions including South Asia, the Middle East and North Africa and Europe, while the trend for suicide-terrorism rates increased significantly in Sub-Saharan Africa. However, no statistically significant changes in the trends in suicide-terrorism rates occurred in three regions including North America, East Asia and Central Asia and Southeast Asia before 9/11, during November 2001 or after 9/11. Originality/value This study indicates the critical importance of the 9/11 terrorist attacks in suicide terrorism and its impact on these events in different regions of the world. The research also provides some recommendations concerning the effectiveness of defensive and offensive counterterrorism policies against suicide terrorism.

scholarly journals Changes of China’s agri-food exports to Germany caused by its accession to WTO and the 2008 financial crisis

2015 ◽  
Vol 7 (2) ◽  
pp. 262-279 ◽  
Author(s):  
Zhichao Guo ◽  
Yuanhua Feng ◽  
Thomas Gries
Keyword(s):  
Time Series ◽  
Financial Crisis ◽  
Regression Models ◽  
Structural Breaks ◽  
Global Financial Crisis ◽  
Middle Part ◽  
Change Points ◽  
Content Type ◽  
The World ◽  
Before And After

Purpose – The purpose of this paper is to investigate changes of China’s agri-food exports to Germany caused by China’s accession to WTO and the global financial crisis in a quantitative way. The paper aims to detect structural breaks and compare differences before and after the change points. Design/methodology/approach – The structural breaks detection procedures in this paper can be applied to find out two different types of change points, i.e. in the middle and at the end of one time series. Then time series and regression models are used to compare differences of trade relationship before and after the detected change points. The methods can be employed in any economic series and work well in practice. Findings – The results indicate that structural breaks in 2002 and 2009 are caused by China’s accession to WTO and the financial crisis. Time series and regression models show that the development of China’s exports to Germany in agri-food products has different features in different sub-periods. Before 1999, there is no significant relationship between China’s exports to Germany and Germany’s imports from the world. Between 2002 and 2008 the former depends on the latter very strongly, and China’s exports to Germany developed quickly and stably. It decreased, however suddenly in 2009, caused by the great reduction of Germany’s imports from the world in that year. But China’s market share in Germany still had a small gain. Analysis of two categories in agri-food trade also leads to similar conclusions. Comparing the two events we see rather different patterns even if they both indicate structural breaks in the development of China’s agri-food exports to Germany. Originality/value – This paper partly originally proposes two statistical algorithms for detecting different kinds of structural breaks in the middle part and at the end of a short-time series, respectively.

scholarly journals USULAN PERMINTAAN PRODUK SN 5 ML DI PT. XYZ DENGAN METODE TIME SERIES

2021 ◽  
Vol 8 (2) ◽  
pp. 117-122
Author(s):  
Sambas Sundana ◽  
Destri Zahra Al Gufronny
Keyword(s):  
Time Series ◽  
Moving Average ◽  
Exponential Smoothing ◽  
Double Exponential ◽  
Double Exponential Smoothing ◽  
Single Exponential ◽  
Weighted Moving Average

Permasalahan yang dihadapi PT. XYZ yaitu kesulitan dalam menentukan jumlah permintaan produk yang harus tersedia untuk periode berikutnya agar tetap dapat memenuhi kebutuhan pelanggan dan tidak menyebabkan penumpukan barang dalam jangka waktu yang lama terutama produk SN 5 ML yang memiliki permintaan jumlah paling besar dari produk lainnya. Tujuan dari penelitian ini yaitu menentukan metode peramalan yang tepat untuk meramalkan jumlah permintaan produk SN 5 ml periode Januari sampai dengan Desember 2021 Metode yang digunakan dalam penelitian ini yaitu metode peramalan Moving Average (MA), Weighted Moving Average (WMA), Single Exponential Smoothing (SES), dan Double Exponential Smoothing (DES). Adapun langkah langkah peramalan yang dilakukan yaitu menentukan tujuan peramalan,memilih unsur apa yang akan diramal, menentukan horizon waktu peramalan (pendek, menengah, atau panjang), memilih tipe model peramalan, mengumpulkan data yang di perlukan untuk melakukan peramalan, memvalidasi dan menerapkan hasil peramalan Berdasarkan perhitungan didapat metode peramalan dengan persentase tingkat kesalahan terkecil dibandingkan dengan metode lainnya yaitu  metode Moving Average (MA) dengan hasil yang diperoleh permintaan produk SN 5 ML pada bulan Januari sampai dengan Desember 2021 yaitu sebanyak 22.844.583 unit

Re-examining the twin deficit hypothesis for major South Asian economies

2019 ◽  
Vol 12 (3) ◽  
pp. 265-287
Author(s):  
Shruti Shastri
Keyword(s):  
Time Series ◽  
Exchange Rate ◽  
Interest Rate ◽  
Sri Lanka ◽  
South Asian ◽  
Current Account ◽  
Transmission Mechanism ◽  
Asian Countries ◽  
Content Type ◽  
Long Run

Purpose The purpose of this study is to revisit the twin deficit hypothesis (TDH) and provide insights into the transmission mechanism connecting budget deficits and current account deficits for five major South Asian countries, namely, India, Bangladesh, Pakistan Sri Lanka and Nepal for the period 1985-2016. Design/methodology/approach This study uses a multivariate framework including real interest rate, real exchange rate and real gross domestic product to avoid the possibility of incorrect inferences caused by omission of relevant mediating variables. The long-run relationship and causality are investigated through the autoregressive distributed lag bounds testing approach and Toda Yamamoto approach, respectively, for each individual country. The robustness of the results is assessed with the help of Westerlund’s cointegration test and group mean fully modified ordinary least squares (GM-FMOLS), group mean dynamic ordinary least square (GM-DOLS) and common correlated effect mean group (CCEMG) estimators in the panel framework. Findings Both time series and panel evidences indicate long-run relationship between budget balance (BB) and current account balance (CAB) together with the mediating variables. The results indicate bi-directional causation between the two balances for India and Bangladesh, TDH for Pakistan and Sri Lanka and the reverse causation from CAB to BB for Nepal. Regarding the transmission mechanism, the results indicate the absence of the causal chain postulated by Mundell–Fleming, which predicts that BB causes CAB via interest rate and exchange rate. A CCEMG estimate of the import demand function reveals a positive government spending elasticity of imports suggesting that BB affects CAB by direct impact through demand. Originality/value This study augments the twin deficit literature on South Asian countries by providing insights into the transmission mechanism connecting the BB and CAB. Moreover, the study provides robust evidences on the TDH by using both time series and panel data techniques.

scholarly journals Modeling and Short-Term Forecasts of Indicators for COVID-19 Outbreak in 25 Countries at the end of March

2020 ◽  
pp. 6-20
Author(s):  
Handan Ankaralı ◽  
Nadire Erarslan ◽  
Özge Pasin ◽  
Abu Kholdun Al Mahmood
Keyword(s):  
Time Series ◽  
Medical Science ◽  
Exponential Smoothing ◽  
Seasonal Effects ◽  
Time Series Models ◽  
Double Exponential ◽  
Wide Range ◽  
Simple Exponential Smoothing ◽  
Double Exponential Smoothing ◽  
Prediction And Forecasting

Objective: The coronavirus, which originated in Wuhan, causing the disease called COVID-19, spread more than 200 countries and continents end of the March. In this study, it was aimed to model the outbreak with different time series models and also predict the indicators. Materials and Methods: The data was collected from 25 countries which have different process at least 20 days. ARIMA(p,d,q), Simple Exponential Smoothing, Holt’s Two Parameter, Brown’s Double Exponential Smoothing Models were used. The prediction and forecasting values were obtained for the countries. Trends and seasonal effects were also evaluated. Results and Discussion: China has almost under control according to forecasting. The cumulative death prevalence in Italy and Spain will be the highest, followed by the Netherlands, France, England, China, Denmark, Belgium, Brazil and Sweden respectively as of the first week of April. The highest daily case prevalence was observed in Belgium, America, Canada, Poland, Ireland, Netherlands, France and Israel between 10% and 12%.The lowest rate was observed in China and South Korea. Turkey was one of the leading countries in terms of ranking these criteria. The prevalence of the new case and the recovered were higher in Spain than Italy. Conclusion: More accurate predictions for the future can be obtained using time series models with a wide range of data from different countries by modelling real time and retrospective data. Bangladesh Journal of Medical Science Vol.19(0) 2020 p.06-20

scholarly journals PERBANDINGAN METODE DOUBLE EXPONENTIAL SMOOTHING HOLT DAN FUZZY TIME SERIES CHEN UNTUK PERAMALAN HARGA PALADIUM

2021 ◽  
Vol 10 (3) ◽  
pp. 325-336
Author(s):  
Anes Desduana Selasakmida ◽  
Tarno Tarno ◽  
Triastuti Wuryandari
Keyword(s):  
Time Series ◽  
Precious Metal ◽  
Exponential Smoothing ◽  
Fuzzy Time Series ◽  
Monthly Data ◽  
Toxic Gases ◽  
Double Exponential ◽  
Speed Up ◽  
Past Data ◽  
Double Exponential Smoothing

Palladium is one of the precious metal commodities with the best performance since 3 years ago. Palladium has many benefits, including being used in the electronics, medical, jewelry and chemical industries. The benefits of palladium in the chemical field are that it can help speed up chemical reactions, filter out toxic gases in exhaust gases, and convert the gas into safer substances, so palladium is usually used as a catalyst for cars. Forecasting is a process of processing past data and projected for future interest using several mathematical models. The model used in this study is the Double Exponential Smoothing Holt and Fuzzy Time Series Chen methods. The process of forecasting palladium prices using monthly data from January 2011 to December 2020 with the Double Exponential Smoothing Holt method and the Fuzzy Time Series Chen method will be carried out in this study to describe the performance of the two methods. Based on the results of the analysis, it can be concluded that the Double Exponential Smoothing Holt and Fuzzy Time Series Chen methods have equally good performance with sMAPE values of 6.21% for Double Exponential Smoothing Holt and 9.554% for Fuzzy Time Series Chen. Forecasting for the next 3 periods using these two methods generally produces forecasting values that are close to the actual data. 

scholarly journals PERAMALAN GARIS KEMISKINAN DI KABUPATEN PURBALINGGA TAHUN 2021-2023 DENGAN METODE DOUBLE EXPONENTIAL SMOOTHING LINIER SATU PARAMETER DARI BROWN

2021 ◽  
Vol 13 (2) ◽  
pp. 155
Author(s):  
Dwi Anggraeni ◽  
Sri Maryani ◽  
Suseno Ariadhy
Keyword(s):  
Time Series ◽  
Poverty Alleviation ◽  
Time Series Data ◽  
Poverty Line ◽  
Exponential Smoothing ◽  
Series Data ◽  
Double Exponential ◽  
Double Exponential Smoothing ◽  
The Government ◽  
Trend Coefficient

Poverty is a major problem in a country. The Indonesian government has made various efforts to tackle the problem of poverty. The main problem faced in poverty alleviation is the large number of people living below the poverty line. Therefore, this study aims to predict the poverty line in Purbalingga Regency for the next three periods as one of the efforts that can be made by the government in poverty alleviation. The method used in this study is a one-parameter linear double exponential smoothing from Brown. The software used in this research is Zaitun Time Series and Microsoft Excel. The steps taken are determining the forecasting objectives, plotting time series data, determining the appropriate method, determining the optimum parameter value, calculating the single exponential smoothing value, calculating double exponential smoothing value, calculate the smoothing constant value, calculate the trend coefficient value and perform forecasting. Based on the calculation results, the optimum alpha parameter value is 0.7 with MAPE value of 1.67866%, which means that this forecasting model has a very good performance. The forecast value of the poverty line in Purbalingga Regency for 2021 is Rp. 396,516, in 2022 it is Rp. 417,818, and in 2023 it is Rp. 439,120.

scholarly journals Peramalan Gelombang Covid 19 Menggunakan Hybrid Nonlinear Regression Logistic – Double Exponential Smoothing di Indonesia dan Prancis

2021 ◽  
Vol 3 (1) ◽  
pp. 37-51
Author(s):  
I Gusti Bagus Ngurah Diksa
Keyword(s):  
Time Series ◽  
Nonlinear Regression ◽  
Forecast Accuracy ◽  
Exponential Smoothing ◽  
Forecasting Model ◽  
Double Exponential ◽  
Single Method ◽  
Double Exponential Smoothing ◽  
The Government ◽  
Second Wave

ABSTRAKIndonesia dan Prancis adalah dua Negara yang mengalami Covid 19 dengan pola pergerakan kasus Covid 19 yang berbeda. Kondisi Indonesia masih mengalami siklus one wave namun Prancis sudah masuk pada pola second wave. Makna second wave adalah kondisi epidemi Covid 19 yang baru muncul setelah epidemi sebelumnya dianggap selesai. Dalam peramalan kasus Covid 19 baik itu terkait informasi puncak dari terjadinya kasus Covid 19 serta ramalan terkait akan berakhirnya pandemi kasus Covid 19 suatu negara merupakan hal penting bagi pemerintah suatu Negara. Model hybrid meningkatkan akurasi ramalan dibandingkan model time series yang dilakukan secara terpisah. Tujuan penelitian ini adalah melakukan peramalan kasus Covid 19 di Indonesia dan Prancis dengan menggunakan metode hybrid dan membandingkan dengan peramalan dengan salah satu metode tunggal. Metode yang digunakan adalah metode tunggal yaitu Nonlinear Regression Logistic dan metode Hybrid Nonlinear Regression Logistic–Double Eksponensial Smoothing. Hasilnya adalah model peramalan Hybrid Nonlinear Regression Logistic and Doubel Exponential Smoothing lebih bagus digunakan dalam peramalan kasus Covid 19 di Indonesia dan Prancis. Terlihat bahwa nilai MAPE model Hybrid Nonlinear Regression Logistic–Double Eksponensial Smoothing jauh lebih kecil dibandingkan model peramalan Nonlinear Regression Logistic. ABSTRACTIndonesia and France are two countries that have experienced Covid 19 with different patterns of movement of Covid 19 cases. Indonesia's condition is still experiencing a one wave cycle but France has entered into the second wave pattern. The meaning of the second wave is the condition of the Covid 19 epidemic which only emerged after the previous epidemic was considered over. In forecasting the Covid 19 case, whether it is related to the peak information on the occurrence of the Covid 19 case and predictions regarding the end of the pandemic of the Covid 19 case in a country, it is important for the government of a country. The hybrid model improves forecast accuracy compared to the time series model which is carried out separately. The purpose of this study is to forecast the cases of Covid 19 in Indonesia and France using the hybrid method and comparing with forecasting with one single method. The method used is a single method, namely Nonlinear Logistic Regression and Hybrid Nonlinear Regression Logistic-Double Exponential Smoothing methods. The result is that the Hybrid Nonlinear Regression Logistic and Double Exponential Smoothing forecasting model is better used in forecasting the Covid 19 cases in Indonesia and France. It can be seen that the MAPE value of the Hybrid Nonlinear Regression Logistic – Double Exponential Smoothing model is much smaller than the Nonlinear Regression Logistic forecasting model.

scholarly journals Peramalan Kurs Rupiah Terhadap US Dollar Menggunakan Metode Hibrid

2017 ◽  
Vol 16 (3) ◽  
pp. 33
Author(s):  
Nyoman Sumerta Yasa ◽  
I Ketut Gede Darma Putra ◽  
N.M.A.E.D Wirastuti
Keyword(s):  
Time Series ◽  
Exponential Smoothing ◽  
Double Exponential ◽  
Radial Basis ◽  
Us Dollar ◽  
Double Exponential Smoothing

Peramalan adalah bagian integral dari kegiatan pengambilan keputusan manajemen. Ramalan yang dilakukan umumnya berdasarkan pada data masa lampau yang dianalisis dengan menggunakan metode- metode tertentu. Oleh karena itu, pada penelitan ini akan meramalkan data time series menggunakan metode Radial Basis Fuction, ARIMA dan Double Exponential Smoothing dengan menggunakan Matlab versi 8.1. Data yang digunakan adalah data kurs jual  harian Rupiah terhadap US Dollar yang dimulai dari bulan Januari 2012 sampai dengan  Maret  2014. Dari  ketiga  hasil   ramalan,     akan digunakan metode voting untuk memperoleh akurasi  dari kondisi menguat atau melemahnya kurs rupiah terhadap US Dollar dan juga hasil ramalan digabungkan dengan metode hibrid. Dari hasil peramalan RBF, ARIMA, Double Exponential Smoothing diperoleh MAPE berturut-turut 0,66%, 3,32% dan 0,94% sedangkan akurasi kondisi menguat melemah sebesar 52,54%, 45,76% dan 52,54%. Dari hasil voting dari kondisi menguat dan melemahnya kurs diperoleh akurasi sebesar 54,24% dan setelah digabungkan dengan metode hibrid diperoleh MAPE sebesar 0,64% dengan akurasi sebesar 50,85%. Dapat dilihat bahwa untuk akurasi kondisi menguat dan melemah yang terbaik diperoleh dengan menggunakan metode voting sedangkan untuk MAPE terbaik diperoleh dengan metode hibrid. Diharapkan penelitian ini dapat membantu dalam menganalisa fluktuasi dari pergerakan nilai mata uang tertentu pada saat transaksi jual – beli valuta asing.

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