Transfer mutual information: A new method for measuring information transfer to the interactions of time series

2017 ◽  
Vol 467 ◽  
pp. 517-526 ◽  
Author(s):  
Xiaojun Zhao ◽  
Pengjian Shang ◽  
Aijing Lin
Keyword(s):  
Time Series ◽  
Mutual Information ◽  
Information Transfer ◽  
New Method

New method for instance or prototype selection using mutual information in time series prediction

2010 ◽  
Vol 73 (10-12) ◽  
pp. 2030-2038 ◽  
Author(s):  
A. Guillen ◽  
L.J. Herrera ◽  
G. Rubio ◽  
H. Pomares ◽  
A. Lendasse ◽  
...  
Keyword(s):  
Time Series ◽  
Mutual Information ◽  
Time Series Prediction ◽  
New Method ◽  
Prototype Selection

Multiscale permutation mutual information quantify the information interaction for traffic time series

2020 ◽  
Vol 102 (3) ◽  
pp. 1909-1923
Author(s):  
Yi Yin ◽  
Xi Wang ◽  
Qiang Li ◽  
Pengjian Shang ◽  
He Gao ◽  
...  
Keyword(s):  
Time Series ◽  
Mutual Information ◽  
Traffic Time Series ◽  
Information Interaction

scholarly journals A New Method for Fusion of Measured and Model-derived Solar Radiation Time-series

2014 ◽  
Vol 48 ◽  
pp. 1617-1626 ◽  
Author(s):  
Theresa Mieslinger ◽  
Felix Ament ◽  
Kaushal Chhatbar ◽  
Richard Meyer
Keyword(s):  
Time Series ◽  
Solar Radiation ◽  
New Method ◽  
Radiation Time

scholarly journals A New Method to Detect Outliers in High-frequency Time Series

2018 ◽  
Vol 8 (1) ◽  
pp. 16
Author(s):  
Ilaria Lucrezia Amerise ◽  
Agostino Tarsitano
Keyword(s):  
Time Series ◽  
High Frequency ◽  
Time Series Data ◽  
New Method ◽  
Series Data ◽  
Absolute Difference ◽  
Data Preparation ◽  
Simple Method ◽  
Pass Through ◽  
Nonparametric Procedure

The objective of this research is to develop a fast, simple method for detecting and replacing extreme spikes in high-frequency time series data. The method primarily consists  of a nonparametric procedure that pursues a balance between fidelity to observed data and smoothness. Furthermore, through examination of the absolute difference between original and smoothed values, the technique is also able to detect and, where necessary, replace outliers with less extreme data. Unlike other filtering procedures found in the literature, our method does not require a model to be specified for the data. Additionally, the filter makes only a single pass through the time series. Experiments  show that the new method can be validly used as a data preparation tool to ensure that time series modeling is supported by clean data, particularly in a complex context such as one with high-frequency data.

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