DISTRIBUTED ADAPTIVE MULTIVARIATE FUNCTION VISUALIZATION

In this article, we introduce a new function visualization method and demonstrate that numerical integration and visualization of multi-dimensional functions are closely related. Adaptive numerical integration is utilized to reduce the number of function evaluations, and generate time series data. The integration region is partitioned into a uniform grid. A grid cell can be sampled many times, or is not sampled at all, depending on the function properties and the integration rule. Function properties are extracted during the process of function evaluation. An aging technique helps visualize functions by retaining the most recently sampled areas and making the older ones transparent. This also results in giving the non-smooth areas more attention than the smooth areas. The new function visualization method gives a view of the whole function while elaborating on important areas such as ridges and troughs, which are critical in many fields, including numerical integration. A Grid service, called Integration Service, is used to solve computationally intensive integration problems. Remote visualization based on the adaptive method helps monitor the progress of a computation, and can be utilized for computational steering. The data are filtered by the server and transferred to the client, which is responsible for visualization mapping and rendering.

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Wavelines: Visualization Method for Comparative Analysis of Time Series Data

International Journal of Hybrid Information Technology ◽

10.14257/ijhit.2016.9.9.38 ◽

2016 ◽

Vol 9 (9) ◽

pp. 423-432

Author(s):

Ruijun Liu ◽

Ning Liu ◽

Yi Chen ◽

Yunfang Zhao ◽

Yang Xu

Keyword(s):

Time Series ◽

Comparative Analysis ◽

Time Series Data ◽

Series Data ◽

Visualization Method ◽

Analysis Of Time Series

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A new visualization method for pairwise time-series data with random walk plot

Proceedings of the 9th International Conference on Ubiquitous Information Management and Communication - IMCOM '15 ◽

10.1145/2701126.2701144 ◽

2015 ◽

Author(s):

Sang-Min Lee ◽

Jinkwan Park ◽

Daegeon Kwon ◽

Bokuk Park ◽

Hwan-Gue Cho ◽

...

Keyword(s):

Time Series ◽

Random Walk ◽

Time Series Data ◽

Series Data ◽

Visualization Method

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Learning Grid Cell Weights for Robust Representation and Effective Classification of Time Series Data

KIISE Transactions on Computing Practices ◽

10.5626/ktcp.2019.25.9.471 ◽

2019 ◽

Vol 25 (9) ◽

pp. 471-476

Author(s):

Junyoung Kang ◽

Heasoo Hwang

Keyword(s):

Time Series ◽

Time Series Data ◽

Grid Cell ◽

Series Data ◽

Robust Representation

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Research on time-series data visualization method based on parameterized parallel coordinates and color mapping function

2012 International Conference on Systems and Informatics (ICSAI2012) ◽

10.1109/icsai.2012.6223048 ◽

2012 ◽

Cited By ~ 1

Author(s):

Zhifang Jiang ◽

Shenghui Cheng ◽

Xiangxu Meng ◽

Zhenhai Zhang

Keyword(s):

Time Series ◽

Data Visualization ◽

Time Series Data ◽

Mapping Function ◽

Series Data ◽

Visualization Method ◽

Parallel Coordinates ◽

Color Mapping

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Graphical Exploratory Data Analysis for Categorical Longitudinal and Time Series Data

PsycEXTRA Dataset ◽

10.1037/e634372013-001 ◽

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

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Some statistical and CI models to predict chaotic high-frequency financial data

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-189107 ◽

2020 ◽

Vol 39 (5) ◽

pp. 6419-6430

Author(s):

Dusan Marcek

Keyword(s):

Time Series Data ◽

Moving Average ◽

Methodological Approach ◽

Back Propagation ◽

Large Data ◽

Series Data ◽

Data Set ◽

Training Time ◽

Optimal Population ◽

Forecast Time

To forecast time series data, two methodological frameworks of statistical and computational intelligence modelling are considered. The statistical methodological approach is based on the theory of invertible ARIMA (Auto-Regressive Integrated Moving Average) models with Maximum Likelihood (ML) estimating method. As a competitive tool to statistical forecasting models, we use the popular classic neural network (NN) of perceptron type. To train NN, the Back-Propagation (BP) algorithm and heuristics like genetic and micro-genetic algorithm (GA and MGA) are implemented on the large data set. A comparative analysis of selected learning methods is performed and evaluated. From performed experiments we find that the optimal population size will likely be 20 with the lowest training time from all NN trained by the evolutionary algorithms, while the prediction accuracy level is lesser, but still acceptable by managers.

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

Jurnal Riset Manajemen Sekolah Tinggi Ilmu Ekonomi Widya Wiwaha Program Magister Manajemen ◽

10.32477/jrm.v1i2.72 ◽

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.

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Economic Turmoil in Islamic Banking Investment

ETIKONOMI ◽

10.15408/etk.v19i2.17206 ◽

2020 ◽

Vol 19 (2) ◽

Author(s):

Budiandru Budiandru ◽

Sari Yuniarti

Keyword(s):

Interest Rates ◽

Stock Price ◽

Time Series Data ◽

Series Data ◽

Islamic Banking ◽

Vector Error Correction ◽

Investment Financing ◽

Vector Error ◽

Operational Activities

Investment financing is one of the operational activities of Islamic banking to encourage the real sector. This study aims to analyze the effect of economic turmoil on investment financing, analyze the response to investment financing, and analyze each variable's contribution in explaining the diversity of investment financing. This study uses monthly time series data from 2009 to 2020 using the Vector Error Correction Model (VECM) analysis. The results show that the exchange rate, inflation, and interest rates significantly affect Islamic banking investment financing in the long term. The response to investment financing is the fastest to achieve stability when it responds to shocks to the composite stock price index. Inflation is the most significant contribution in explaining diversity in investment financing. Islamic banking should increase the proportion of funding for investment. Customers can have a larger business scale to encourage economic growth, with investment financing increasing.JEL Classification: E22, G11, G24How to Cite:Budiandru., & Yuniarti, S. (2020). Economic Turmoil in Islamic Banking Investment. Etikonomi: Jurnal Ekonomi, 19(2), xx – xx. https://doi.org/10.15408/etk.v19i2.17206.

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