The symbolic method for time series based on mean and slope

2015 ◽  
pp. 271-276
Keyword(s):  
Time Series ◽  
Symbolic Method

Distinguishing Stock Indices and Detecting Economic Crises Based on Weighted Symbolic Permutation Entropy

2019 ◽  
Vol 18 (04) ◽  
pp. 1950026 ◽  
Author(s):  
Ningning Zhang ◽  
Yupeng Sun ◽  
Yongbo Zhang ◽  
Pengbo Yang ◽  
Aijing Lin ◽  
...  
Keyword(s):  
Time Series ◽  
Stock Markets ◽  
Permutation Entropy ◽  
Aviation Industry ◽  
Economic Crises ◽  
Step Process ◽  
Extended Technique ◽  
Stock Indices ◽  
Symbolic Method ◽  
Symbolic Process

Permutation entropy (PE) is evolved as an important way to reveal the intrinsic characteristics of complex systems. While PE does not consider that the amplitudes of identical order sequences might be different, weighted permutation entropy (WPE), the extended technique, weighs each vector to alleviate this problem. Furthermore, experimental investigation shows that symbolizing the time series contributes to detecting sudden dynamical changes. As an exemplary measure, we study weighted symbolic permutation entropy (WSPE) which is a combination of WPE and threshold-dependent symbolic entropy to mount the intrinsic information of four stock indices from China and America stock markets, and six sector indices of banking, aviation industry and pharmacy from China stock markets. One thing needs to be especially pointed out is that symbolic method we use in this work is not the most common method which divides time series into some small bins, but a two-step process, including symbolic process and coding process. The statistical analysis in stock indices shows that WSPE method is capable of distinguishing stock indices and detecting economic crises.

The LDE-Type Flare Occurrence (1969-1993)

1994 ◽  
Vol 144 ◽  
pp. 279-282
Author(s):  
A. Antalová
Keyword(s):  
Time Series ◽  
Fourier Analysis ◽  
Sunspot Cycle ◽  
List Type ◽  
Type Number ◽  
Solar Cycles ◽  
Type Iv ◽  
Long Duration ◽  
Cycle Maximum ◽  
Flare Index

AbstractThe occurrence of LDE-type flares in the last three cycles has been investigated. The Fourier analysis spectrum was calculated for the time series of the LDE-type flare occurrence during the 20-th, the 21-st and the rising part of the 22-nd cycle. LDE-type flares (Long Duration Events in SXR) are associated with the interplanetary protons (SEP and STIP as well), energized coronal archs and radio type IV emission. Generally, in all the cycles considered, LDE-type flares mainly originated during a 6-year interval of the respective cycle (2 years before and 4 years after the sunspot cycle maximum). The following significant periodicities were found:• in the 20-th cycle: 1.4, 2.1, 2.9, 4.0, 10.7 and 54.2 of month,• in the 21-st cycle: 1.2, 1.6, 2.8, 4.9, 7.8 and 44.5 of month,• in the 22-nd cycle, till March 1992: 1.4, 1.8, 2.4, 7.2, 8.7, 11.8 and 29.1 of month,• in all interval (1969-1992):a)the longer periodicities: 232.1, 121.1 (the dominant at 10.1 of year), 80.7, 61.9 and 25.6 of month,b)the shorter periodicities: 4.7, 5.0, 6.8, 7.9, 9.1, 15.8 and 20.4 of month.Fourier analysis of the LDE-type flare index (FI) yields significant peaks at 2.3 - 2.9 months and 4.2 - 4.9 months. These short periodicities correspond remarkably in the all three last solar cycles. The larger periodicities are different in respective cycles.

Automatic Identification of Autoregressive Integrated Moving Average Time Series

1982 ◽  
Vol 14 (3) ◽  
pp. 156-166 ◽  
Author(s):  
Chin-Sheng Alan Kang ◽  
David D. Bedworth ◽  
Dwayne A. Rollier
Keyword(s):  
Time Series ◽  
Moving Average ◽  
Automatic Identification ◽  
Autoregressive Integrated Moving Average

Smoothing Facilitates the Detection of Coupled Responses in Psychophysiological Time Series

2000 ◽  
Vol 14 (1) ◽  
pp. 1-10 ◽  
Author(s):  
Joni Kettunen ◽  
Niklas Ravaja ◽  
Liisa Keltikangas-Järvinen
Keyword(s):  
Time Series ◽  
Type I Error ◽  
Electrodermal Activity ◽  
Moving Average ◽  
Type I ◽  
Facial Electromyography ◽  
Response Characteristics ◽  
Smoothing Methods ◽  
Response Systems ◽  
Different Response

Abstract We examined the use of smoothing to enhance the detection of response coupling from the activity of different response systems. Three different types of moving average smoothers were applied to both simulated interbeat interval (IBI) and electrodermal activity (EDA) time series and to empirical IBI, EDA, and facial electromyography time series. The results indicated that progressive smoothing increased the efficiency of the detection of response coupling but did not increase the probability of Type I error. The power of the smoothing methods depended on the response characteristics. The benefits and use of the smoothing methods to extract information from psychophysiological time series are discussed.

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