Non-Linear Properties of the VIX Index

2017 ◽  
Vol 5 (2) ◽  
pp. 16-24
Author(s):  
L. Magafas ◽  
M. Hanias ◽  
A. Tavlatou ◽  
P. Kostantaki
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Strange Attractor ◽  
Series Analysis ◽  
The World ◽  
Non Linear ◽  
The Future ◽  
Stock Indexes

This paper applies non-linear methods to analyze and predict the daily VIX index which is one of the most important stock indexes in the world. The aim of the analysis is to quantitatively show if the corresponding time series is a deterministic chaotic one and if one or more days ahead prediction can be achieved. The research employs Grassberger and Procaccia's methodology in the time series analysis in order to estimate the correlation and minimum embedding dimensions of the corresponding strange attractor. To achieve from the sample a multistep ahead prediction, the article gives the average for overall neighbours' projections of k-steps into the future. These results make the present work a valuable tool for traders, investors, and funds.

Nonlinear Time Series Analysis with R

2018 ◽  
Author(s):  
Ray Huffaker ◽  
Marco Bittelli ◽  
Rodolfo Rosa
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Particle Physics ◽  
Linear Time ◽  
Nonlinear Time Series ◽  
Nonlinear Time Series Analysis ◽  
Series Analysis ◽  
Linear Behavior ◽  
Non Linear ◽  
Scientific Principles

In the process of data analysis, the investigator is often facing highly-volatile and random-appearing observed data. A vast body of literature shows that the assumption of underlying stochastic processes was not necessarily representing the nature of the processes under investigation and, when other tools were used, deterministic features emerged. Non Linear Time Series Analysis (NLTS) allows researchers to test whether observed volatility conceals systematic non linear behavior, and to rigorously characterize governing dynamics. Behavioral patterns detected by non linear time series analysis, along with scientific principles and other expert information, guide the specification of mechanistic models that serve to explain real-world behavior rather than merely reproducing it. Often there is a misconception regarding the complexity of the level of mathematics needed to understand and utilize the tools of NLTS (for instance Chaos theory). However, mathematics used in NLTS is much simpler than many other subjects of science, such as mathematical topology, relativity or particle physics. For this reason, the tools of NLTS have been confined and utilized mostly in the fields of mathematics and physics. However, many natural phenomena investigated I many fields have been revealing deterministic non linear structures. In this book we aim at presenting the theory and the empirical of NLTS to a broader audience, to make this very powerful area of science available to many scientific areas. This book targets students and professionals in physics, engineering, biology, agriculture, economy and social sciences as a textbook in Nonlinear Time Series Analysis (NLTS) using the R computer language.

Analyses and anticipating the future trend of accidents in an electricity distribution company of Iran: A time series analysis

Work ◽  
2021 ◽  
pp. 1-6
Author(s):  
Shirin Nasrollah Nejhad ◽  
Tayebeh Ilaghinezhad Bardsiri ◽  
Maryam feiz arefi ◽  
Amin babaei poya ◽  
Ehsan mazloumi ◽  
...  
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Occupational Injuries ◽  
Model Management ◽  
High Rate ◽  
Electricity Distribution ◽  
Temporary Workers ◽  
Series Analysis ◽  
The Future ◽  
Distribution Company

BACKGROUND: Many work-related fatalities happen every year in electricity distribution companies. This study was conducted to model accidents using the time series analysis and survey descriptive factors of injuries in an electricity distribution company in Tehran, Iran. METHODS: Data related to 2010 to 2017 were collected from the database of the safety department. Time Series and trend analysis were used for data analyzing and anticipating the accidents up to 2022. RESULT: Most of the accidents occurred in summer. Workers’ negligence was the reason for 75%of deaths. Employment type and type of injuries had a significant relationship (p <  0.05). CONCLUSION: The anticipating model indicated occupational injuries are going to have an increase in the future. A high rate of accidents in summer maybe because of the warm weather or insufficient skills in temporary workers. Temporary workers have no chance to work in a year like permanent workers, therefore acquisition experiences may be less in them. Based on the estimating model, Management should pay attention to those sectors of the company where most of the risky activities take place. Also, training programs and using personal protective equipment can help to protect workers in hazardous conditions.

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 Impact of campaign-style delivery of routine vaccines during Intensified Mission Indradhanush in India: a controlled interrupted time-series analysis

2020 ◽  
Author(s):  
Emma Clarke-Deelder ◽  
Christian Suharlim ◽  
Susmita Chatterjee ◽  
Logan Brenzel ◽  
Arindam Ray ◽  
...  
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Immunization Coverage ◽  
Interrupted Time Series ◽  
Childhood Vaccination ◽  
World Health ◽  
Interrupted Time Series Analysis ◽  
Series Analysis ◽  
The World ◽  
The Impact

AbstractIntroductionThe world is not on track to achieve the goals for immunization coverage and equity described by the World Health Organization’s Global Vaccine Action Plan. In India, only 62% of children had received a full course of basic vaccines in 2016. We evaluated the Intensified Mission Indradhanush (IMI), a campaign-style intervention to increase routine immunization coverage and equity in India, implemented in 2017-2018.MethodsWe conducted a comparative interrupted time-series analysis using monthly district-level data on vaccine doses delivered, comparing districts participating and not participating in IMI. We estimated the impact of IMI on coverage and under-coverage (defined as the proportion of children who were unvaccinated) during the four-month implementation period and in subsequent months.FindingsDuring implementation, IMI increased delivery of thirteen infant vaccines by between 1.6% (95% CI: −6.4, 10.2%) and 13.8% (3.0%, 25.7%). We did not find evidence of a sustained effect during the 8 months after implementation ended. Over the 12 months from the beginning of implementation, IMI reduced under-coverage of childhood vaccination by between 3.9% (−6.9%, 13.7%) and 35.7% (−7.5%, 77.4%). The largest estimated effects were for the first doses of vaccines against diptheria-tetanus-pertussis and polio.InterpretationIMI had a substantial impact on infant immunization delivery during implementation, but this effect waned after implementation ended. Our findings suggest that campaign-style interventions can increase routine infant immunization coverage and reach formerly unreached children in the shorter term, but other approaches may be needed for sustained coverage improvements.FundingBill & Melinda Gates Foundation.

Time-series analysis of tides and similar motions of the sea surface

1967 ◽  
Vol 4 (1) ◽  
pp. 103-112 ◽  
Author(s):  
D. E. Cartwright
Keyword(s):  
Time Series ◽  
Solar Radiation ◽  
Time Series Analysis ◽  
Sea Level ◽  
Sea Surface ◽  
Survey Article ◽  
Series Analysis ◽  
Non Linear ◽  
Exciting Forces ◽  
Linear Effects

This survey article, most of whose results are described in greater detail in Munk and Cartwright (1966), which will hereafter be abbreviated to MC, describes methods which aim to separate the response of the sea level at a given place due to various exciting forces such as gravity, solar radiation, non-linear effects, and weather. In so doing, it provides predictors for sea level which are formally simpler and somewhat more accurate than those given by the classical methods.

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