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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Calculation of Average Mutual Information (AMI) and False-Nearest Neighbors (FNN) for the Estimation of Embedding Parameters of Multidimensional Time Series in Matlab
|
|---|---|
| Published in |
Frontiers in Psychology, September 2018
|
| DOI | 10.3389/fpsyg.2018.01679 |
| Pubmed ID | |
| Authors |
Sebastian Wallot, Dan Mønster |
| Abstract |
Using the method or time-delayed embedding, a signal can be embedded into higher-dimensional space in order to study its dynamics. This requires knowledge of two parameters: The delay parameter τ, and the embedding dimension parameter D. Two standard methods to estimate these parameters in one-dimensional time series involve the inspection of the Average Mutual Information (AMI) function and the False Nearest Neighbor (FNN) function. In some contexts, however, such as phase-space reconstruction for Multidimensional Recurrence Quantification Analysis (MdRQA), the empirical time series that need to be embedded already possess a dimensionality higher than one. In the current article, we present extensions of the AMI and FNN functions for higher dimensional time series and their application to data from the Lorenz system coded in Matlab. |
X Demographics
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Netherlands | 1 | 25% |
| India | 1 | 25% |
| Unknown | 2 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 4 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 161 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 161 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 28 | 17% |
| Researcher | 22 | 14% |
| Student > Master | 13 | 8% |
| Student > Bachelor | 11 | 7% |
| Student > Doctoral Student | 10 | 6% |
| Other | 27 | 17% |
| Unknown | 50 | 31% |
| Readers by discipline | Count | As % |
|---|---|---|
| Engineering | 36 | 22% |
| Neuroscience | 14 | 9% |
| Agricultural and Biological Sciences | 6 | 4% |
| Computer Science | 6 | 4% |
| Sports and Recreations | 6 | 4% |
| Other | 36 | 22% |
| Unknown | 57 | 35% |
Attention Score in Context
This research output has an Altmetric Attention Score of 2. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 14 June 2022.
All research outputs
#15,977,084
of 26,589,560 outputs
Outputs from Frontiers in Psychology
#15,730
of 35,529 outputs
Outputs of similar age
#188,594
of 351,820 outputs
Outputs of similar age from Frontiers in Psychology
#418
of 745 outputs
Altmetric has tracked 26,589,560 research outputs across all sources so far. This one is in the 38th percentile – i.e., 38% of other outputs scored the same or lower than it.
So far Altmetric has tracked 35,529 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 13.9. This one has gotten more attention than average, scoring higher than 54% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 351,820 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 44th percentile – i.e., 44% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 745 others from the same source and published within six weeks on either side of this one. This one is in the 41st percentile – i.e., 41% of its contemporaries scored the same or lower than it.