Fast correlation coefficient estimation algorithm for HBase-based massive time series data

2019 ◽  
Vol 13 (4) ◽  
pp. 864-878
Author(s):  
Wen Liu ◽  
Tuqian Zhang ◽  
Yanming Shen ◽  
Peng Wang
Keyword(s):  
Time Series ◽  
Correlation Coefficient ◽  
Time Series Data ◽  
Estimation Algorithm ◽  
Series Data ◽  
Coefficient Estimation

scholarly journals DISPARITAS PEMBANGUNAN DAN FAKTOR-FAKTOR YANG MEMPENGARUHINYA (STUDI KASUS DI DAERAH SUMBAGSEL)

2007 ◽  
Vol 1 (1) ◽  
pp. 21
Author(s):  
Daryono Soebagiyo
Keyword(s):  
Time Series ◽  
Regional Development ◽  
Correlation Coefficient ◽  
Time Series Data ◽  
Influence Factors ◽  
Series Data ◽  
Government Revenue ◽  
The Government

This is well illustrated by recent research into inter-regional development growth disparities. Some researchers have followed the Neoclassical route, emphasizing the role of the Williamson Index, and then can be expressed relationship in general form that in regression and correlation coefficient analysis involving time series data. The objectives of this research was to preview the classification development of disparities and influence factors in the late five years during 1992-1996, case study in SUMBAGSEL. The Analysis can be calculated to measure the government revenue, income regional and contributed tax sectors.

scholarly journals A New Period-Sequential Index Forecasting Algorithm for Time Series Data

2019 ◽  
Vol 9 (20) ◽  
pp. 4386 ◽  
Author(s):  
Hongyan Jiang ◽  
Dianjun Fang ◽  
Klaus Spicher ◽  
Feng Cheng ◽  
Boxing Li
Keyword(s):  
Time Series ◽  
Correlation Coefficient ◽  
Time Series Data ◽  
Moving Average ◽  
Series Data ◽  
Forecasting Accuracy ◽  
Autoregressive Integrated Moving Average ◽  
New Period ◽  
Psi Method

A period-sequential index algorithm with sigma-pi neural network technology, which is called the (SPNN-PSI) method, is proposed for the prediction of time series datasets. Using the SPNN-PSI method, the cumulative electricity output (CEO) dataset, Volkswagen sales (VS) dataset, and electric motors exports (EME) dataset are tested. The results show that, in contrast to the moving average (MA), exponential smoothing (ES), and autoregressive integrated moving average (ARIMA) methods, the proposed SPNN-PSI method shows satisfactory forecasting quality due to lower error, and is more suitable for the prediction of time series datasets. It is also concluded that: There is a trend that the higher the correlation coefficient value of the reference historical datasets, the higher the prediction quality of SPNN-PSI method, and a higher value (>0.4) of correlation coefficient for SPNN-PSI method can help to improve occurrence probability of higher forecasting accuracy, and produce more accurate forecasts for the big datasets.

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.

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