FINANCIAL BINARY BETTING, STYLES, VALUATIONS AND DEDUCTIONS FROM DATA

2012 ◽  
Vol 1 (2) ◽  
pp. 127-146
Author(s):  
Peter Oliver
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Financial Derivatives ◽  
Point Of View ◽  
Series Data ◽  
Standard Methods ◽  
New Form ◽  
Market Expectations

A relatively new form of financial spread betting, the binary bet, has become popular.  Part of the popularity of this style of bet, from the gambler’s point of view, is undoubtedly due to the simplicity and transparency of the contracts.  The fact that these bets are free at the time they are taken is an added inducement.  For the bet provider, as long as the correct buy and sell levels are maintained during the betting period and the betting frequency on any contract is high, it is again relatively simple to ensure a known income from the operation.Binary spread bets are examples of financial derivatives and the standard methods used in that field can be used to deduce the parameters that should apply.  This gives useful information to the gamblers in telling them how much they are paying for the bet.  Watching how the quotes are moving in time can also inform how the gambling community is behaving and what the average view of the outcome is.A variety of types of binary bets are valued and in many cases it is possible to derive analytic formulas.  These can be applied to time series data that are acquired from quotes and used to deduce information about the bets held by a provider and the market expectations of the community.

Chaotic Characteristics of Workface Gas Emission Time-Series Data

2013 ◽  
Vol 340 ◽  
pp. 456-460 ◽  
Author(s):  
Mei Ying Qiao ◽  
Jian Yi Lan
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Chaotic Behavior ◽  
Chaotic Time Series ◽  
Point Of View ◽  
Series Data ◽  
Embedding Dimension ◽  
Minimum Entropy ◽  
Gas Emission ◽  
Emission Time

The chaotic time series phase space reconstruction theory based in this paper. First, the appropriate embedding dimension and delay time are selected by minimum entropy rate. Followed the chaotic behavior are analyzed by the use of the Poincare section map and Power spectrum of time series from the qualitative point of view. Based on NLSR LLE the quantitative study of the chaotic time series characteristics indicators is proposed. Finally, the gas emission workface of Hebi 10th Mine Coal is studied. The several analytical results of the above methods show that: the gas emission time-series data of this workface has chaotic characteristics.

scholarly journals Analysis of multidimensional geophysical monitoring time series for earthquake prediction

1999 ◽  
Vol 42 (5) ◽  
Author(s):  
A. A. Lyubushin
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Time Windows ◽  
Point Of View ◽  
Series Data ◽  
Geophysical Monitoring ◽  
Strong Earthquakes ◽  
Multidimensional Time Series ◽  
Geophysical Time Series ◽  
Parameter Values

A method is presented for detection of synchronous signals in multidimensional time series data. It is based on estimation of eigenvalues of spectral matrices and canonical coherences in moving time windows and extraction of an aggregated signal (a scalar signal, which accumulates in its own variations only those spectral components which are present simultaneously in each scalar time series). It is known that an increase in the collective behavior of the components of some systems and an enlarged spatial radius of fluctuations of their parameters could be regarded as an important precursor of an oncoming catastrophe, i.e. abrupt change of the system's parameter values. From that point of view, detection of synchronous signals in various geophysical parameters, measured at points of some network, covering a given area of the Earth's crust, is of interest for identifying precursors of strong earthquakes. Some examples are presented of the use of this technique in the processing of real geophysical time series.

CURRENCY EXCHANGE RATE PREDICTION TECHNIQUE BY FUZZY INFERENCING ON THE CHAOTIC NATURE OF TIME SERIES DATA

1996 ◽  
Vol 04 (05) ◽  
pp. 431-448 ◽  
Author(s):  
SABYASACHI GHOSHRAY
Keyword(s):  
Time Series ◽  
Dynamic System ◽  
Exchange Rates ◽  
Foreign Exchange ◽  
Time Series Data ◽  
Point Of View ◽  
Series Data ◽  
Dimensional Euclidean Space ◽  
Reconstruction Method ◽  
Foreign Exchange Rates

Predicting foreign exchange rates and stock market indices have been a well researched topic in the field of financial engineering. However, most methods suffer from serious drawback due to the inherent uncertainty in the data acquisition process. Here, we have analyzed the very nature of the time series data from a pure dynamic system point of view and explored the deterministic chaotic characteristic in it. In this research, the concept of chaos has been analyzed thoroughly and the relationships among chaos, stability and order have been explained with respect to the concept of time. A method of predicting time series data based on deterministic dynamically system has been presented in this monograph. The present research revolves around the concepts of embedding and fuzzy reconstruction. In this regard, the necessary and sufficient condition for this reconstruction of the state space of the dynamic system in a multi-dimensional Euclidean space has been substantiated in accordance to Theory of embedding. Finally, a fuzzy reconstruction method based on fuzzy multiple regression analysis method has been used to predict the foreign exchange rates with accuracy.

Time Series Analysis and Data Prediction: An ECM Neuronal Approach Applied to EUR/USD Currency

2014 ◽  
Vol 918 ◽  
pp. 301-306
Author(s):  
Dusan Marcek
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Statistical Models ◽  
Time Series Data ◽  
Rbf Neural Network ◽  
Point Of View ◽  
Neural Nets ◽  
Series Data ◽  
Forecasting Models ◽  
Data Prediction

Several approaches to dynamic modeling in economic such as ARIMA, GARCH, neural nets and error corrected models have become popular in recent years. We evaluate statistical and neuronal methods for daily EUR/USD currency prediction using daily EUR/USD time series data. Both techniques are reviewed and contrasted from the accuracy of forecasting models point of view. We show that an RBF neural network can achieve better prediction results than the latest statistical methodologies. Following fruitful applications of neural networks to predict financial data this work goes ahead by using neural networks for modeling any non-linearities within the estimated statistical models.

scholarly journals Entropy-Based Strategies for Rapid Pre-Processing and Classification of Time Series Data from Single-Molecule Force Experiments

Entropy ◽  
2020 ◽  
Vol 22 (6) ◽  
pp. 701
Author(s):  
Denis Horvath ◽  
Gabriel Žoldák
Keyword(s):  
Time Series ◽  
Optical Tweezers ◽  
Single Molecule ◽  
Time Series Data ◽  
Uncertain Data ◽  
Molecular State ◽  
Point Of View ◽  
Series Data ◽  
Processing Information ◽  
Automated Processing

Recent advances in single-molecule science have revealed an astonishing number of details on the microscopic states of molecules, which in turn defined the need for simple, automated processing of numerous time-series data. In particular, large datasets of time series of single protein molecules have been obtained using laser optical tweezers. In this system, each molecular state has a separate time series with a relatively uneven composition from the point of view-point of local descriptive statistics. In the past, uncertain data quality and heterogeneity of molecular states were biased to the human experience. Because the data processing information is not directly transferable to the black-box-framework for an efficient classification, a rapid evaluation of a large number of time series samples simultaneously measured may constitute a serious obstacle. To solve this particular problem, we have implemented a supervised learning method that combines local entropic models with the global Lehmer average. We find that the methodological combination is suitable to perform a fast and simple categorization, which enables rapid pre-processing of the data with minimal optimization and user interventions.

Graphical Exploratory Data Analysis for Categorical Longitudinal and Time Series Data

2013 ◽  
Author(s):  
Stephen J. Tueller ◽  
Richard A. Van Dorn ◽  
Georgiy Bobashev ◽  
Barry Eggleston
Keyword(s):  
Time Series ◽  
Data Analysis ◽  
Exploratory Data Analysis ◽  
Time Series Data ◽  
Series Data ◽  
Exploratory Data

Faktor-Faktor Yang Mempengaruhi Nilai Tukar Dollar Amerika Serikat Terhadap Rupiah Tahun 2000–2013

2017 ◽  
Vol 1 (2) ◽  
pp. 177-191
Author(s):  
Rizki Rahma Kusumadewi ◽  
Wahyu Widayat
Keyword(s):  
Time Series ◽  
Exchange Rate ◽  
Money Supply ◽  
Time Series Data ◽  
The United States ◽  
Economic Conditions ◽  
Series Data ◽  
Arch Model ◽  
United States Dollar ◽  
The Exchange Rate

Exchange rate is one tool to measure a country’s economic conditions. The growth of a stable currency value indicates that the country has a relatively good economic conditions or stable. This study has the purpose to analyze the factors that affect the exchange rate of the Indonesian Rupiah against the United States Dollar in the period of 2000-2013. The data used in this study is a secondary data which are time series data, made up of exports, imports, inflation, the BI rate, Gross Domestic Product (GDP), and the money supply (M1) in the quarter base, from first quarter on 2000 to fourth quarter on 2013. Regression model time series data used the ARCH-GARCH with ARCH model selection indicates that the variables that significantly influence the exchange rate are exports, inflation, the central bank rate and the money supply (M1). Whereas import and GDP did not give any influence.

An Anomaly Detection Method with Exemplar Subsequence for Time Series Data

2016 ◽  
Vol 136 (3) ◽  
pp. 363-372
Author(s):  
Takaaki Nakamura ◽  
Makoto Imamura ◽  
Masashi Tatedoko ◽  
Norio Hirai
Keyword(s):  
Time Series ◽  
Anomaly Detection ◽  
Time Series Data ◽  
Detection Method ◽  
Series Data

The Comparison of Land Subsidence between East and West Side of Bangkok, Thailand

2020 ◽  
Vol 17 (3) ◽  
pp. 1
Author(s):  
Angkana Pumpuang ◽  
Anuphao Aobpaet
Keyword(s):  
Time Series ◽  
Land Subsidence ◽  
Time Series Data ◽  
Series Data ◽  
Industrial Estates ◽  
Vertical Displacements ◽  
Land Deformation ◽  
Extensive Farming ◽  
Chao Phraya River ◽  
Set Up

The land deformation in line of sight (LOS) direction can be measured using time series InSAR. InSAR can successfully measure land subsidence based on LOS in many big cities, including the eastern and western regions of Bangkok which is separated by Chao Phraya River. There are differences in prosperity between both sides due to human activities, land use, and land cover. This study focuses on the land subsidence difference between the western and eastern regions of Bangkok and the most possible cause affecting the land subsidence rates. The Radarsat-2 single look complex (SLC) was used to set up the time series data for long term monitoring. To generate interferograms, StaMPS for Time Series InSAR processing was applied by using the PSI algorithm in DORIS software. It was found that the subsidence was more to the eastern regions of Bangkok where the vertical displacements were +0.461 millimetres and -0.919 millimetres on the western and the eastern side respectively. The districts of Nong Chok, Lat Krabang, and Khlong Samwa have the most extensive farming area in eastern Bangkok. Besides, there were also three major industrial estates located in eastern Bangkok like Lat Krabang, Anya Thani and Bang Chan Industrial Estate. By the assumption of water demand, there were forty-eight wells and three wells found in the eastern and western part respectively. The number of groundwater wells shows that eastern Bangkok has the demand for water over the west, and the pumping of groundwater is a significant factor that causes land subsidence in the area.Keywords: Subsidence, InSAR, Radarsat-2, Bangkok

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