scholarly journals Coronavirus (COVID-19) Pandemic in Indonesia: Cases Overview and Daily Data Time Series using Naïve Forecast Method

2021 ◽  
Vol 3 (1) ◽  
pp. 1-8
Author(s):  
Annisa Puspa Kirana ◽  
Adhitya Bhawiyuga
Keyword(s):  
Time Series ◽  
Percentage Error ◽  
Forecast Method ◽  
Monthly Basis ◽  
Average Case ◽  
Monthly Average ◽  
Daily Data ◽  
Time Series Dataset ◽  
The World ◽  
Forecasting Analysis

At the end of December 2019, the virus emerges from Wuhan, China, and resulted in a severe outbreak in many cities in China and expanding globally, including Indonesia. Indonesia is the fourth most populated country globally. As of February 2021, Indonesia in the first rank of positive cases of COVID-19 in Southeast Asia, number 4 in Asia, and number 19 in the world. Our paper aims to provide detailed reporting and analysis of the COVID-19 case overview and forecasting that have hit Indonesia. Our time-series dataset from March 2020 to January 2021. Summary of cases studied included the number of positive cases and deaths due to COVID-19 on a daily or monthly basis. We use time series and forecasting analysis using the Naïve Forecast method.  The prediction is daily case prediction for six months starting from February 1, 2021, to June 30, 2021, using active cases daily COVID-19 data in all provinces in Indonesia. The highest monthly average case prediction is in June, which is 35,662 cases. Our COVID-19 prediction study has a mean absolute percentage error (MAPE) score of 15.85%.

scholarly journals Delay Time Parameter and Its Confidence Interval of Predictive Time Series of COVID-19 Outbreak Between the First and the Second Wave

2021 ◽  
Vol 53 (2) ◽  
pp. 305-322
Author(s):  
Rapin Sunthornwat ◽  
Sirikanlaya Sookkhee
Keyword(s):  
Time Series ◽  
Confidence Interval ◽  
South Korea ◽  
Growth Curve ◽  
Delay Time ◽  
Time Parameter ◽  
Coefficient Of Determination ◽  
Percentage Error ◽  
The World ◽  
Second Wave

The outbreak of coronavirus disease 2019 (COVID-19) has become a major problem facing humans all around the world. For governments, in order to deal with the outbreak and protect the population, it is important to predict the number of infectious cases in the future to monitor the COVID-19 situation. This research aimed to compare the effectiveness of the logistic and the delay logistic time series in predicting the total number of infectious cases by using actual data from four countries, i.e. Thailand, South Korea, Egypt, and Nigeria. The total number of COVID-19 cases was collected during the first and the second wave of the COVID-19 outbreak. The validation and accuracy of the predictive growth curve time series were determined based on statistical values, i.e. the coefficient of determination and the root mean squared percentage error. It was found that the logistic time series was more appropriate for predicting the first wave in the four countries. For the second wave, the delay logistic time series was preferable. Moreover, the confidence interval based on Chebyshev’s inequality of delay time between the first and the second wave of the COVID-19 outbreak is also proposed.

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 Using fuzzy time series with and without markov chain: to forecast of edible bird nest exported from Indonesia

2022 ◽  
Vol 335 ◽  
pp. 00016
Author(s):  
Osfar Sjofjan ◽  
Danung Nur Adli
Keyword(s):  
Time Series ◽  
Markov Chain ◽  
Secondary Data ◽  
Fuzzy Time Series ◽  
Percentage Error ◽  
Mean Square ◽  
Forecast Method ◽  
Short Term ◽  
Bird Nest ◽  
Markov Chain Method

Edible bird nest (EBN) were traditional medicine consumed by the Tiongkok. This study compared two-algorithm method. Fuzzy time series and Markov chain as forecast method the number of bird nest exported from Indonesia. The secondary data between 2012 and 2018 were from Bureau Central Statistic (BPS). The scope using in this study were bird nest between 2012 until 2018, with a unit of volume kilograms (Kg). Used secondary export data, collected from BPS of Indonesia. Data were analysed using Fuzzy Time Series with and without Markov Chain using R Studio. The result showed that Fuzzy Time Series with and without Markov Chain method performs better in the forecasting ability in short-term period prediction and the values of Mean Absolute Percentage Error (MAPE) and Mean Square Error (MSE) tends to be smaller than the Fuzzy Time Series without Markov Chain. It can be concluded the number of exported can be used Fuzzy time series.

scholarly journals Real-Time Forecasting of COVID-19 prevalence in India using ARIMA Model

2020 ◽  
Vol 4 (10) ◽  
pp. 78-83
Keyword(s):  
Time Series ◽  
Virus Disease ◽  
Moving Average ◽  
Time Series Prediction ◽  
Arima Model ◽  
Autoregressive Integrated Moving Average ◽  
Daily Data ◽  
The World ◽  
Increasing Trend ◽  
Short Term Prediction

Corona virus disease (COVID -19) has changed the world completely due to unavailability of its exact treatment. It has affected 215 countries in the world in which India is no exception where COVID patients are increasing exponentially since 15th of Feb. The objective of paper is to develop a model which can predict daily new cases in India. The autoregressive integrated moving average (ARIMA) models have been used for time series prediction. The daily data of new COVID-19 cases act as an exogenous variable in this framework. The daily data cover the sample period of 15th February, 2020 to 24th May, 2020. The time variable under study is a non-stationary series as 𝒚𝒕 is regressed with 𝒚𝒕−𝟏 and the coefficient is 1. The time series have clearly increasing trend. Results obtained revealed that the ARIMA model has a strong potential for short-term prediction. In PACF graph. Lag 1 and Lag 13 is significant. Regressed values implies Lag 1 and Lag 13 is significant in predicting the current values. The model predicted maximum COVID-19 cases in India at around 8000 during 5thJune to 20th June period. As per the model, the number of new cases shall start decreasing after 20th June in India only. The results will help governments to make necessary arrangements as per the estimated cases. The limitation of this model is that it is unable to predict jerks on either lower or upper side of daily new cases. So, in case of jerks re-estimation will be required.

scholarly journals Effects of government policies and the Nowruz holidays on confirmed COVID-19 cases in Iran: An intervention time series analysis

PLoS ONE ◽  
2021 ◽  
Vol 16 (8) ◽  
pp. e0256516
Author(s):  
Ali Hadianfar ◽  
Razieh Yousefi ◽  
Milad Delavary ◽  
Vahid Fakoor ◽  
Mohammad Taghi Shakeri ◽  
...  
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Government Policies ◽  
World Health ◽  
Social Distancing ◽  
Social Interventions ◽  
Series Analysis ◽  
Daily Data ◽  
The World ◽  
Health Organization

Background Public health policies with varying degrees of restriction have been imposed around the world to prevent the spread of coronavirus disease 2019 (COVID-19). In this study, we aimed to evaluate the effects of the implementation of government policies and the Nowruz holidays on the containment of the COVID-19 pandemic in Iran, using an intervention time series analysis. Methods Daily data on COVID-19 cases registered between February 19 and May 2, 2020 were collected from the World Health Organization (WHO)’s website. Using an intervention time series modeling, the effect of two government policies on the number of confirmed cases were evaluated, namely the closing of schools and universities, and the implementation of social distancing measures. Furthermore, the effect of the Nowruz holidays as a non-intervention factor for the spread of COVID-19 was also analyzed. Results The results showed that, after the implementation of the first intervention, i.e., the closing of universities and schools, no statistically significant change was found in the number of new confirmed cases. The Nowruz holidays was followed by a significant increase in new cases (1,872.20; 95% CI, 1,257.60 to 2,476.79; p<0.001)), while the implementation of social distancing measures was followed by a significant decrease in such cases (2,182.80; 95% CI, 1,556.56 to 2,809.04; p<0.001). Conclusion The Nowruz holidays and the implementation of social distancing measures in Iran were related to a significant increase and decrease in COVID-19 cases, respectively. These results highlight the necessity of measuring the effect of health and social interventions for their future implementations.

scholarly journals PERAMALAN JUMLAH PENUMPANG PESAWAT BANDARA I GUSTI NGURAH RAI MENGGUNAKAN EXPONENTIAL SMOOTHING DAN RUEY-CHYN TSAUR

2021 ◽  
Vol 10 (4) ◽  
pp. 222
Author(s):  
WILDAN FATTURAHMAN MUJTABA ◽  
I GUSTI AYU MADE SRINADI ◽  
I WAYAN SUMARJAYA
Keyword(s):  
Time Series ◽  
Exponential Smoothing ◽  
Small Error ◽  
Fuzzy Time Series ◽  
Percentage Error ◽  
Forecast Method ◽  
Tourist Destination ◽  
Forecasting Methods ◽  
Airline Passengers ◽  
Better Than

Bali province is a tourist destination island with good transportation. Airplane is the most used transportation to go to Bali. Convenience of the airline passengers are the most important thing for I Gusti Ngurah Rai Airport Authorithy. An exact forecast method is needed to predict the numbers of passenger in the future. There are two types of forecasting methods; triple exponential smoothing and Fuzzy Time Series Ruey-Chyn Tsaur, however based on the research Fuzzy Time Series Ruey-Chyn Tsaur is better than triple exponential smoothing due to a small error MAPE (Mean Absolute Percentage Error) of 2,4% and plot is close to actual data.

scholarly journals Forecasting Gold Prices in India using Time series and Deep Learning Algorithms

2021 ◽  
Vol 10 (5) ◽  
pp. 21-27
Author(s):  
P Sai Shankar ◽  
M Krishna Reddy
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Learning Algorithm ◽  
Arima Model ◽  
Absolute Error ◽  
Percentage Error ◽  
Deep Learning Algorithm ◽  
Primary Object ◽  
Daily Data ◽  
Forecasting Performance

The primary object of this paper is to compare the traditional time series models with deep learning algorithm. The ARIMA model is developed to forecast Indian Gold prices using daily data for the period 2016 to 2020 obtained from World Gold Council. We fitted the ARIMA (2,1,2) model which exhibited the least AIC values. In the meanwhile, MLP, CNN and LSTM models are also examined to forecast the gold prices in India. Mean absolute error, mean absolute percentage error and root mean squared errors used to evaluate the forecasting performance of the models. Hence, LSTM model superior than that of the other three models for forecasting the gold prices in India.

scholarly journals The statistical analysis of daily data associated with different parameters of the New Coronavirus COVID-19 pandemic in Georgia and their short-term interval prediction from September 2020 to February 2021

2021 ◽  
Author(s):  
Avtandil G. Amiranashvili ◽  
Ketevan R. Khazaradze ◽  
Nino D. Japaridze
Keyword(s):  
Time Series ◽  
Statistical Analysis ◽  
Confidence Interval ◽  
Infection Rate ◽  
Maximum Effect ◽  
Regression Equations ◽  
Daily Mortality ◽  
Daily Data ◽  
The World ◽  
Upper Level

AbstractIn the autumn - winter period of 2020, very difficult situation arose in Georgia with the course of the pandemic of the New Coronavirus COVID-19. In particular, in November-December period of 2020, Georgia eight days was rank a first in the world in terms of COVID-19 infection rate per 1 million populations.In this work results of a statistical analysis of the daily data associated with New Coronavirus COVID-19 infection of confirmed (C), recovered (R), deaths (D) and infection rate (I) cases of the population of Georgia in the period from September 01, 2020 to February 28, 2021 (for I - from December 05, 2020 to February 28, 2021) are presented. It also presents the results of the analysis of ten-day (decade) and two-week forecasting of the values of C, D and I, the information was regularly sent to the National Center for Disease Control & Public Health of Georgia and posted on the Facebook page https://www.facebook.com/Avtandil1948/.The analysis of data is carried out with the use of the standard statistical analysis methods of random events and methods of mathematical statistics for the non-accidental time-series of observations. In particular, the following results were obtained.Georgia’s ranking in the world for Covid-19 infection and deaths from September 1, 2020 to February 28, 2021 (per 1 million population) was determined. Georgia was in the first place: Infection - November 21, 22, 27, 28 and December 04, 05, 06, 09, 2020; Death - November 22, 2020.A comparison between the daily mortality from Covid-19 in Georgia from September 1, 2020 to February 28, 2021 with the average daily mortality rate in 2015-2019 was made. The largest share value of D from mean death in 2015-2019 was 36.9% (19.12.2020), the smallest - 0.9% (21.09.2020, 24.09.2020 - 26.09.2020).The statistical analysis of the daily and decade data associated with coronavirus COVID-19 pandemic of confirmed, recovered, deaths cases and infection rate of the population of Georgia are carried out. Maximum daily values of investigation parameters are following: C = 5450 (05.12.2020), R = 4599 (21.12.2020), D = 53 (19.12.2020), I = 30.1 % (05.12.2020). Maximum mean decade values of investigation parameters are following: C = 4337 (1 Decade of December 2020), R = 3605 (3 Decade of November 2020), D = 44 (2 Decade of December 2020), I = 26.8 % (1 Decade of December 2020).It was found that the regression equations for the time variability of the daily values of C, R and D have the form of a tenth order polynomial.Mean values of speed of change of confirmed -V(C), recovered - V(R) and deaths - V(D) coronavirus-related cases in different decades of months from September 2020 to February 2021 were determined. Maximum mean decade values of investigation parameters are following: V(C) = +104 cases/day (1 Decade of November 2020), V(R) = +94 cases/day (3 Decade of October and 1 Decade of November 2020), V(D) = +0.9 cases/day (1 Decade of November 2020).Cross-correlations analysis between confirmed COVID-19 cases with recovered and deaths cases from 05.12.2020 to 28.02.2021 is carried out. So, the maximum effect of recovery is observed 13-14 days after infection, and deaths - after 13-14 and 17-18 days.The scale of comparing real data with the predicted ones and assessing the stability of the time series of observations in the forecast period in relation to the pre-predicted one was offered.Comparison of real and calculated predictions data of C (23.09.2020-28.02.2021), D (01.01.2021-28.02.2021) and I (01.02.2021-28.02.2021) in Georgia are carried out. It was found that daily, mean decade and two-week real values of C, D and I practically falls into the 67% - 99.99% confidence interval of these predicted values for the specified time periods (except the forecast of C for 13.10.2020-22.10.2020, when a nonlinear process of growth of C values was observed and its real values have exceeded 99.99% of the upper level of the confidence interval of forecast).Alarming deterioration with the spread of coronavirus parameters may arise when their daily values are higher 99.99% of upper level of the forecast confidence interval. Excellent improvement - when these daily values are below 99.99% of the lower level of the forecast confidence interval.The lockdown introduced in Georgia on November 28, 2020 brought positive results. There are clearly positive tendencies in the spread of COVID-19 to February 2021.

Forecasting based on spectral time series analysis: prediction of the Aurubis stock price

2020 ◽  
Vol 17 (4) ◽  
pp. 215-227
Author(s):  
Julia Babirath ◽  
Karel Malec ◽  
Rainer Schmitl ◽  
Kamil Maitah ◽  
Mansoor Maitah
Keyword(s):  
Time Series ◽  
Risk Management ◽  
Time Series Analysis ◽  
Wiener Process ◽  
Stock Price ◽  
Percentage Error ◽  
Portfolio Performance ◽  
Stock Price Prediction ◽  
Series Analysis ◽  
Augmented Dickey Fuller

The attempt to predict stock price movements has occupied investors ever since. Reliable forecasts are a basis for investment management, and improved forecasting results lead to enhanced portfolio performance and sound risk management. While forecasting using the Wiener process has received great attention in the literature, spectral time series analysis has been disregarded in this respect. The paper’s main objective is to evaluate whether spectral time series analysis can produce reliable forecasts of the Aurubis stock price. Aurubis poses a suitable candidate for an investor’s portfolio due to its sound economic and financial situation and the steady dividend policy. Additionally, reliable management contributes to making Aurubis an investment opportunity. To judge if the achieved forecast results can be considered satisfactory, they are compared against the simulation results of a Wiener process. After de-trending the time series using an Augmented Dickey-Fuller test, the residuals were compartmentalized into sine and cosine functions. The frequencies, amplitude, and phase were obtained using the Fast Fourier transform. The mean absolute percentage error measured the accuracy of the stock price prediction, and the results showed that the spectral analysis was able to deliver superior results when comparing the simulation using a Wiener process. Hence, spectral time series can enhance stock price forecasts and consequently improve risk management.

scholarly journals A Bayesian Model to Forecast the Time Series Kinetic Energy Data for a Power System

Energies ◽  
2021 ◽  
Vol 14 (11) ◽  
pp. 3299
Author(s):  
Ashish Shrestha ◽  
Bishal Ghimire ◽  
Francisco Gonzalez-Longatt
Keyword(s):  
Time Series ◽  
Kinetic Energy ◽  
Power System ◽  
Bayesian Model ◽  
Arima Model ◽  
Power Converter ◽  
Series Data ◽  
Percentage Error ◽  
Optimal Size ◽  
Short Term

Withthe massive penetration of electronic power converter (EPC)-based technologies, numerous issues are being noticed in the modern power system that may directly affect system dynamics and operational security. The estimation of system performance parameters is especially important for transmission system operators (TSOs) in order to operate a power system securely. This paper presents a Bayesian model to forecast short-term kinetic energy time series data for a power system, which can thus help TSOs to operate a respective power system securely. A Markov chain Monte Carlo (MCMC) method used as a No-U-Turn sampler and Stan’s limited-memory Broyden–Fletcher–Goldfarb–Shanno (LM-BFGS) algorithm is used as the optimization method here. The concept of decomposable time series modeling is adopted to analyze the seasonal characteristics of datasets, and numerous performance measurement matrices are used for model validation. Besides, an autoregressive integrated moving average (ARIMA) model is used to compare the results of the presented model. At last, the optimal size of the training dataset is identified, which is required to forecast the 30-min values of the kinetic energy with a low error. In this study, one-year univariate data (1-min resolution) for the integrated Nordic power system (INPS) are used to forecast the kinetic energy for sequences of 30 min (i.e., short-term sequences). Performance evaluation metrics such as the root-mean-square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and mean absolute scaled error (MASE) of the proposed model are calculated here to be 4.67, 3.865, 0.048, and 8.15, respectively. In addition, the performance matrices can be improved by up to 3.28, 2.67, 0.034, and 5.62, respectively, by increasing MCMC sampling. Similarly, 180.5 h of historic data is sufficient to forecast short-term results for the case study here with an accuracy of 1.54504 for the RMSE.

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