A new correlation coefficient for bivariate time-series data

2014 ◽  
Vol 414 ◽  
pp. 274-284 ◽  
Author(s):  
Orhan Erdem ◽  
Elvan Ceyhan ◽  
Yusuf Varli
Keyword(s):  
Time Series ◽  
Correlation Coefficient ◽  
Time Series Data ◽  
Series Data ◽  
New Correlation ◽  
Bivariate Time Series

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.

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