Time Series Modelling and Prediction of the Coronavirus Outbreaks (COVID-19) in the World

2021 ◽  
pp. 27-55
Author(s):  
Mohsen Maleki
Keyword(s):  
Time Series ◽  
Time Series Modelling ◽  
The World

scholarly journals Bernoulli Time Series Modelling with Application to Accommodation Tourism Demand

2021 ◽  
Vol 5 (1) ◽  
pp. 17
Author(s):  
Miguel Ángel Ruiz Reina
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Prognostic Models ◽  
Series Data ◽  
Economic Agents ◽  
Time Series Modelling ◽  
Simulation And Training ◽  
Uncertainty Method ◽  
And Training ◽  
Hotel Accommodation

In this research, a new uncertainty method has been developed and applied to forecasting the hotel accommodation market. The simulation and training of Time Series data are from January 2001 to December 2018 in the Spanish case. The Log-log BeTSUF method estimated by GMM-HAC-Newey-West is considered as a contribution for measuring uncertainty vs. other prognostic models in the literature. The results of our model present better indicators of the RMSE and Ratio Theil’s for the predictive evaluation period of twelve months. Furthermore, the straightforward interpretation of the model and the high descriptive capacity of the model allow economic agents to make efficient decisions.

scholarly journals Business Days Time Series Weekly Trend and Seasonality

2021 ◽  
Vol 5 (1) ◽  
pp. 26
Author(s):  
Karlis Gutans
Keyword(s):  
Time Series ◽  
Global Warming ◽  
Exchange Rates ◽  
Temperature Data ◽  
Adjustment Method ◽  
Currency Exchange ◽  
Currency Exchange Rates ◽  
Time Series Decomposition ◽  
The World

The world changes at incredible speed. Global warming and enormous money printing are two examples, which do not affect every one of us equally. “Where and when to spend the vacation?”; “In what currency to store the money?” are just a few questions that might get asked more frequently. Knowledge gained from freely available temperature data and currency exchange rates can provide better advice. Classical time series decomposition discovers trend and seasonality patterns in data. I propose to visualize trend and seasonality data in one chart. Furthermore, I developed a calendar adjustment method to obtain weekly trend and seasonality data and display them in the chart.

scholarly journals Did the UN convention on the rights of the child reduce child mortality around the world? An interrupted time series analysis

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Christopher A. Tait ◽  
Abtin Parnia ◽  
Nishan Zewge-Abubaker ◽  
Wendy H. Wong ◽  
Heather Smith-Cannoy ◽  
...  
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Child Mortality ◽  
Interrupted Time Series ◽  
Interrupted Time Series Analysis ◽  
Series Analysis ◽  
Rights Of The Child ◽  
Reduce Child Mortality ◽  
The World

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.

scholarly journals The duration of the active phase on surge-type glaciers: contrasts between Svalbard and other regions

1991 ◽  
Vol 37 (127) ◽  
pp. 388-400 ◽  
Author(s):  
Julian A. Dowdeswell ◽  
Gordon S. Hamilton ◽  
Jon Ove Hagen
Keyword(s):  
Time Series ◽  
North America ◽  
Active Phase ◽  
Aerial Photographs ◽  
Polar Ice ◽  
Phase Duration ◽  
Field Surveys ◽  
The World ◽  
Glacier Surges ◽  
Fast Motion

AbstractMany glaciers in Svalbard and in other glacierized areas of the world are known to surge. However, the time series of observations required to assess the duration of fast motion is very restricted. Data on active-phase duration in Svalbard come from aerial photographs, satellite imagery, field surveys and airborne reconnaissance. Evidence on surge duration is available for eight Svalbard ice masses varying from 3 to 1250 km2. Worldwide, active-phase duration is recorded for less than 50 glaciers. Few observations are available on high polar ice masses. The duration of the active phase is significantly longer for Svalbard glaciers than for surge-type glaciers in other areas from which data are available. In Svalbard, the active phase may last from 3 to 10 years. By contrast, a surge duration of 1–2 years is more typical of ice masses in northwest North America, Iceland and the Pamirs. Ice velocities during the protracted active phase on Svalbard glaciers are considerably lower than those for many surge-type glaciers in these other regions. Mass is transferred down-glacier more slowly but over a considerably longer period. Svalbard surge-type glaciers do not exhibit the very abrupt termination of the active phase, over periods of a few days, observed for several Alaskan glaciers. The duration of the active phase in Svalbard is not dependent on parameters related to glacier size. The quiescent phase is also relatively long (50–500 years) for Svalbard ice masses. Detailed field monitoring of changing basal conditions through the surge cycle is required from surge-type glaciers in Svalbard in order to explain the significantly longer length of the active phase for glaciers in the archipelago, which may also typify other high polar ice masses. The finding that surge behaviour, in the form of active-phase duration, shows systematic differences between different regions and their environments has important implications for understanding the processes responsible for glacier surges.

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.

TIME SERIES MODELLING OF EPIDEMICS: LEADING INDICATORS, CONTROL GROUPS AND POLICY ASSESSMENT

2021 ◽  
Vol 257 ◽  
pp. 83-100
Author(s):  
Andrew Harvey
Keyword(s):  
Time Series ◽  
Data Science ◽  
Leading Indicators ◽  
Control Groups ◽  
Balanced Growth ◽  
Time Series Modelling ◽  
Key Variables ◽  
The Uk ◽  
The Relationship

This article shows how new time series models can be used to track the progress of an epidemic, forecast key variables and evaluate the effects of policies. The univariate framework of Harvey and Kattuman (2020, Harvard Data Science Review, Special Issue 1—COVID-19, https://hdsr.mitpress.mit.edu/pub/ozgjx0yn) is extended to model the relationship between two or more series and the role of common trends is discussed. Data on daily deaths from COVID-19 in Italy and the UK provides an example of leading indicators when there is a balanced growth. When growth is not balanced, the model can be extended by including a non-stationary component in one of the series. The viability of this model is investigated by examining the relationship between new cases and deaths in the Florida second wave of summer 2020. The balanced growth framework is then used as the basis for policy evaluation by showing how some variables can serve as control groups for a target variable. This approach is used to investigate the consequences of Sweden’s soft lockdown coronavirus policy in the spring of 2020.

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