scholarly journals A Visibility Graph Approach to CNY Exchange Rate Networks and Characteristic Analysis

2017 ◽  
Vol 2017 ◽  
pp. 1-17 ◽  
Author(s):  
Can-Zhong Yao ◽  
Ji-Nan Lin
Keyword(s):  
Time Series ◽  
Exchange Rate ◽  
Small World ◽  
Graph Algorithm ◽  
Visibility Graph ◽  
Characteristic Analysis ◽  
Scale Free ◽  
Us Dollar ◽  
Path Lengths ◽  
Time Point

We find that exchange rate networks are significantly similar from the perspective of topological structure, though with relatively great differences in fluctuation characteristics from perspective of exchange rate time series. First, we transform central parity rate time series of US dollar, Euro, Yen, and Sterling against CNY into exchange rate networks with visibility graph algorithm and find consistent topological characteristics in four exchange rate networks, with their average path lengths 5 and average clustering coefficients 0.7. Further, we reveal that all four transformed exchange rate networks show hierarchical structure and small-world and scale-free properties, with their hierarchy indexes 0.5 and power exponents 1.5. Both of the US dollar network and Sterling network exhibit assortative mixing features, while the Euro network and Yen network exhibit disassortative mixing features. Finally, we research community structure of exchange rate networks and uncover the fact that the communities are actually composed by large amounts of continuous time point fractions and small amounts of discrete time point fractions. In this way, we can observe that the spread of time series values corresponding to nodes inside communities is significantly lower than the spread of those values corresponding to nodes of the whole networks.

scholarly journals Examination of Chaotic Structures in Semiconductor or Alloy Voltage Time-Series: A Complex Network Approach for the Case of TlInTe2

Physics ◽  
2020 ◽  
Vol 2 (4) ◽  
pp. 624-639
Author(s):  
Dimitrios Tsiotas ◽  
Lykourgos Magafas ◽  
Michael P. Hanias
Keyword(s):  
Time Series ◽  
Network Analysis ◽  
Complex Network ◽  
Graph Algorithm ◽  
Differential Resistance ◽  
Visibility Graph ◽  
Characteristic Region ◽  
Scale Free ◽  
Complex Network Analysis ◽  
Voltage Oscillation

This paper proposes a method for examining chaotic structures in semiconductor or alloy voltage oscillation time-series, and focuses on the case of the TlInTe2 semiconductor. The available voltage time-series are characterized by instabilities in negative differential resistance in the current–voltage characteristic region, and are primarily chaotic in nature. The analysis uses a complex network analysis of the time-series and applies the visibility graph algorithm to transform the available time-series into a graph so that the topological properties of the graph can be studied instead of the source time-series. The results reveal a hybrid lattice-like configuration and a major hierarchical structure corresponding to scale-free characteristics in the topology of the visibility graph, which is in accordance with the default hybrid chaotic and semi-periodic structure of the time-series. A novel conceptualization of community detection based on modularity optimization is applied to the available time-series and reveals two major communities that are able to be related to the pair-wise attractor of the voltage oscillations’ phase portrait of the TlInTe2 time-series. Additionally, the network analysis reveals which network measures are more able to preserve the chaotic properties of the source time-series. This analysis reveals metric information that is able to supplement the qualitative phase-space information. Overall, this paper proposes a complex network analysis of the time-series as a method for dealing with the complexity of semiconductor and alloy physics.

scholarly journals Analyzing Levels of Concern About Joint Punishment for Dishonesty Using the Visibility Graph Network

2021 ◽  
Vol 9 ◽  
Author(s):  
Zhiqiang Qu ◽  
Yujie Zhang ◽  
Fan Li
Keyword(s):  
Time Series ◽  
Topological Structure ◽  
Time Series Data ◽  
Graph Algorithm ◽  
Visibility Graph ◽  
Series Data ◽  
Scale Free ◽  
Central Node ◽  
Important Means ◽  
Search Index

Joint punishment for dishonesty is an important means of administrative regulation. This research analyzed the dynamic characteristics of time series data from the Baidu search index using the keywords “joint punishment for dishonesty” based on a visibility graph network. Applying a visibility graph algorithm, time series data from the Baidu Index was transformed into complex networks, with parameters calculated to analyze the topological structure. Results showed differences in the use of joint punishment for dishonesty in certain provinces by calculating the parameters of the time series network from January 1, 2020 to May 27, 2021; it was also shown that most of the networks were scale-free. Finally, the results of K-means clustering showed that the 31 provinces (excluding Hong Kong, Macao and Taiwan) can be divided into four types. Meanwhile, by analyzing the national Baidu Index data from 2020 to May 2021, the period of the time series data and the influence range of the central node were found.

scholarly journals US Dollar/Turkish Lira Exchange Rate Forecasting Model Based on Deep Learning Methodologies and Time Series Analysis

Symmetry ◽  
2020 ◽  
Vol 12 (9) ◽  
pp. 1553
Author(s):  
Harun Yasar ◽  
Zeynep Hilal Kilimci
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Exchange Rate ◽  
Time Series Analysis ◽  
Word Embedding ◽  
Learning Models ◽  
Exchange Rate Forecasting ◽  
Us Dollar ◽  
Turkish Lira ◽  
Embedding Methods

Exchange rate forecasting has been an important topic for investors, researchers, and analysts. In this study, financial sentiment analysis (FSA) and time series analysis (TSA) are proposed to form a predicting model for US Dollar/Turkish Lira exchange rate. For this purpose, the proposed hybrid model is constructed in three stages: obtaining and modeling text data for FSA, obtaining and modeling numerical data for TSA, and blending two models like a symmetry. To our knowledge, this is the first study in the literature that uses social media platforms as a source for FSA and blends them with TSA methods. To perform FSA, word embedding methods Word2vec, GloVe, fastText, and deep learning models such as CNN, RNN, LSTM are used. To the best of our knowledge, this study is the first attempt in terms of performing the FSA by using the combinations of deep learning models with word embedding methods for both Turkish and English texts. For TSA, simple exponential smoothing, Holt–Winters, Holt’s linear, and ARIMA models are employed. Finally, with the usage of the proposed model, any user who wants to make a US Dollar/Turkish Lira exchange rate forecast will be able to make a more consistent and strong exchange rate forecast.

MIXED MULTIFRACTAL ANALYSIS OF CRUDE OIL, GOLD AND EXCHANGE RATE SERIES

Fractals ◽  
2016 ◽  
Vol 24 (04) ◽  
pp. 1650046 ◽  
Author(s):  
MEIFENG DAI ◽  
SHUXIANG SHAO ◽  
JIANYU GAO ◽  
YU SUN ◽  
WEIYI SU
Keyword(s):  
Time Series ◽  
Fractal Dimension ◽  
Exchange Rate ◽  
Crude Oil ◽  
Multifractal Analysis ◽  
Multifractal Spectrum ◽  
Price Series ◽  
The Us ◽  
Multifractal Spectra ◽  
Us Dollar

The multifractal analysis of one time series, e.g. crude oil, gold and exchange rate series, is often referred. In this paper, we apply the classical multifractal and mixed multifractal spectrum to study multifractal properties of crude oil, gold and exchange rate series and their inner relationships. The obtained results show that in general, the fractal dimension of gold and crude oil is larger than that of exchange rate (RMB against the US dollar), reflecting a fact that the price series in gold and crude oil are more heterogeneous. Their mixed multifractal spectra have a drift and the plot is not symmetric, so there is a low level of mixed multifractal between each pair of crude oil, gold and exchange rate series.

DYNAMICAL ANALYSES USING VISIBILITIES IN FINANCIAL MARKETS

Fractals ◽  
2016 ◽  
Vol 24 (02) ◽  
pp. 1650016 ◽  
Author(s):  
SEUNGSIK MIN ◽  
KYUSEONG LIM ◽  
KI-HO CHANG ◽  
IL-HWAN PARK ◽  
KYUNGSIK KIM
Keyword(s):  
Time Series ◽  
Financial Markets ◽  
Statistical Method ◽  
Poisson Distribution ◽  
Power Law ◽  
Graph Algorithm ◽  
Visibility Graph ◽  
Financial Networks ◽  
Topological Properties ◽  
Network Metrics

In this paper, the network metrics are studied in a time series of the KOSPI and the KOSDAQ indices converting by the visibility graph algorithm. The degree distributions for the KOSPI and the KOSDAQ are proportional to a power law rather than the Poisson distribution. Since we mainly simulate and analyze the network metrics from the nodes and its links, our result cannot be found unambiguously to have universal and characteristic properties of statistical quantities via financial networks. Particularly, these topological properties may improve by implementing the statistical method and its technique from altered data of financial networks.

FORECASTING MEXICAN PESO - US DOLLAR EXCHANGE RATE AFTER THE 2020 PANDEMIC WITH GARCH AND XGBOOST TIME SERIES MODELS

2020 ◽  
Vol 65 (1) ◽  
pp. 89-106
Author(s):  
Alejandro Ruiz-Olivares ◽  
Martha Elva Ramírez-Guzmán ◽  
Sandy Yaredd Trujano-Ramos
Keyword(s):  
Time Series ◽  
Exchange Rate ◽  
Time Series Models ◽  
Dollar Exchange Rate ◽  
Us Dollar ◽  
Mexican Peso

IMPROVED WEIGHT FUZZY TIME SERIES AS USED IN THE EXCHANGE RATES FORECASTING OF US DOLLAR TO RINGGIT MALAYSIA

2013 ◽  
Vol 12 (01) ◽  
pp. 1350005 ◽  
Author(s):  
RISWAN EFENDI ◽  
ZUHAIMY ISMAIL ◽  
MUSTAFA MAT DERIS
Keyword(s):  
Time Series ◽  
Exchange Rate ◽  
Exchange Rates ◽  
Fuzzy Time Series ◽  
Business Environments ◽  
Forecasting Method ◽  
The Us ◽  
Us Dollar ◽  
Daily Forecast ◽  
Fuzzy Logical

Foreign exchange rate (forex) forecasting has been the subject of several rigorous investigations due to its importance in evaluating the benefits and risks of the international business environments. Many methods have been researched with the ultimate goal being to increase the reliability and efficiency of the forecasting method. However as the data are inherently dynamic and complex, the development of accurate forecasting method remains a challenging task if not a formidable one. This paper proposes a new weight of the fuzzy time series model for a daily forecast of the exchange rate market. Through this method, the weights are assigned to the fuzzy relationships based on a probability approach. This can be implemented to carry out the frequently recurring fuzzy logical relationship (FLR) in the fuzzy logical group (FLG). The US dollar to the Malaysian Ringgit (MYR) exchange rates are used as an example and the efficiency of the proposed method is compared with the methods proposed by Yu and Cheng et al. The result shows that the proposed method has enhanced the accuracy and efficiency of the daily exchange rate forecasting opportunities.

scholarly journals PENGARUH VARIABEL DOMESTIK DAN GLOBAL TERHADAP NILAI TUKAR RUPIAH

2019 ◽  
Vol 1 (2) ◽  
pp. 341
Author(s):  
Pamela Dwi Hapsari ◽  
Melti Roza Adry
Keyword(s):  
Economic Growth ◽  
Time Series ◽  
Exchange Rate ◽  
Exchange Rates ◽  
Interest Rates ◽  
Secondary Data ◽  
American Interest ◽  
Library Studies ◽  
Us Dollar ◽  
Positive Effect

This study aims to find out how the influence of domestic and global variables on changes in the exchange rate of the rupiah per US dollar. The data used are secondary data in the form of time series from 2008: Q1 to 2018: Q3, with documentation data collection techniques and library studies obtained from relevant institutions and agencies. The variables used are Exchange Rates of Rp/USD (Y), Indonesian Economic Growth (X1), Indonesian Interest Rates (X2), American Economic Growth (X3) and American Interest Rates (X4). The research methods used are: (1) Ordinary Last Square (OLS), (2) Classical Assumption Test. The results of the study show that (1) Indonesian Economic Growth has a negative and significant effect on the rupiah exchange rate per US dollar. (2) Indonesian interest rates do not have a significant influence on the rupiah exchange rate per US dollar. (3) American Economic Growth has a positive and significant effect on the rupiah exchange rate per US dollar. (4) American interest rates have a positive effect on the rupiah exchange rate per US dollar.

The structure of real-world networks

2018 ◽  
Author(s):  
Mark Newman
Keyword(s):  
Power Law ◽  
Real World ◽  
Small World ◽  
Component Structure ◽  
Scale Free ◽  
Scale Free Networks ◽  
Path Lengths ◽  
Power Law Distributions

This chapter brings together the ideas and techniques developed in previous chapters, applying them to a range of real-world networks to describe and understand the structure of those networks. Topics discussed include the observed component structure of networks, average path lengths between nodes and the small-world effect, degree distributions including power-law distributions and scale-free networks, clustering and transitivity, and assortative mixing.

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