A novel two-dimensional correlation coefficient for assessing associations in time series data

2017 ◽  
Vol 37 (11) ◽  
pp. 4065-4076 ◽  
Author(s):  
Fatih Dikbaş
Keyword(s):  
Time Series ◽  
Correlation Coefficient ◽  
Time Series Data ◽  
Series Data ◽  
Two Dimensional

scholarly journals Multi-scale analysis of linear data in a two-dimensional space

2013 ◽  
Vol 13 (3) ◽  
pp. 248-265 ◽  
Author(s):  
Yi Qiang ◽  
Seyed H Chavoshi ◽  
Steven Logghe ◽  
Philippe De Maeyer ◽  
Nico Van de Weghe
Keyword(s):  
Time Series ◽  
Visual Analysis ◽  
Time Series Data ◽  
Dimensional Space ◽  
Series Data ◽  
Multiple Time ◽  
Two Dimensional ◽  
Map Algebra ◽  
Real World Datasets ◽  
Triangular Model

Many disciplines are faced with the problem of handling time-series data. This study introduces an innovative visual representation for time series, namely the continuous triangular model. In the continuous triangular model, all subintervals of a time series can be represented in a two-dimensional continuous field, where every point represents a subinterval of the time series, and the value at the point is derived through a certain function (e.g. average or summation) of the time series within the subinterval. The continuous triangular model thus provides an explicit overview of time series at all different scales. In addition to time series, the continuous triangular model can be applied to a broader sense of linear data, such as traffic along a road. This study shows how the continuous triangular model can facilitate the visual analysis of different types of linear data. We also show how the coordinate interval space in the continuous triangular model can support the analysis of multiple time series through spatial analysis methods, including map algebra and cartographic modelling. Real-world datasets and scenarios are employed to demonstrate the usefulness of this approach.

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.

Band Centers and Track Finding for Large Two-Dimensional Infrared Spectroscopic Arrays

2003 ◽  
Vol 57 (3) ◽  
pp. 323-330 ◽  
Author(s):  
Li Chen ◽  
Marc Garland
Keyword(s):  
Time Series ◽  
Spectral Data ◽  
Time Series Data ◽  
Series Data ◽  
Second Derivative ◽  
Two Dimensional ◽  
Peak Finding ◽  
Derivative Method ◽  
Computationally Intensive ◽  
Second Derivative Method

An efficient two-dimensional (2D) peak-finding algorithm is proposed to find peak maps that specify the peak centers of all bands in two-dimensional arrays of time-series infrared spectral data. The algorithm combines the second-derivative method with the intrinsic characteristics of 2D infrared reaction spectral data. Initially, the second-derivative method is used to detect all possible peak center positions, and then three criteria drawn from characteristics of 2D continuous spectral data are employed to filter peak positions. Four 2D peak maps are generated in a sequential order, with better and better approximations to the peak center positions being obtained in each. The 2D peak-finding algorithm has been successfully applied to both simulated spectra (to initially evaluate the algorithm) and then real 2D experimental spectra. The resulting peak maps exhibit very good estimates of the peak center positions. An ordering from the most significant to the least significant bands is obtained. The final peak maps can be used as starting parameters for various applications including the computationally intensive curve-fitting of time-series data.

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