|
View: |
Part 1: Document Description
|
|
Citation |
|
|---|---|
|
Title: |
Runtime Data of Horizontal Visibility Algorithms for synthetic and empirical Time Series |
|
Identification Number: |
doi:10.26249/FK2/MXFPLV |
|
Distributor: |
osnaData |
|
Date of Distribution: |
2022-12-19 |
|
Version: |
2 |
|
Bibliographic Citation: |
Schmidt, Jonas, 2022, "Runtime Data of Horizontal Visibility Algorithms for synthetic and empirical Time Series", https://doi.org/10.26249/FK2/MXFPLV, osnaData, V2 |
|
Citation |
|
|
Title: |
Runtime Data of Horizontal Visibility Algorithms for synthetic and empirical Time Series |
|
Identification Number: |
doi:10.26249/FK2/MXFPLV |
|
Authoring Entity: |
Schmidt, Jonas (Osnabrück University) |
|
Distributor: |
osnaData |
|
Access Authority: |
Schmidt, Jonas |
|
Depositor: |
Schmidt, Jonas |
|
Date of Deposit: |
2022-12-16 |
|
Study Scope |
|
|
Keywords: |
Computer and Information Science, Graph Theory, Computational Complexity Theory, Horizontal Visibility Graphs |
|
Abstract: |
This repository contains the computed runtimes of state-of-the-art horizontal visibility algorithms for synthetic and empirical time series. The provided Python code was used to compute the runtime data and includes implementations of various horizontal visibility algorithms. The main contribution is a newly developed algorithm that extends the fast weighted horizontal visibility algorithm of Zhu et al. . The proposed algorithm works efficiently on streamed data, is multi-processing capable, and has linear runtime in the worst case. |
|
Methodology and Processing |
|
|
Sources Statement |
|
|
Data Access |
|
|
Notes: |
CC0 Waiver |
|
Other Study Description Materials |
|
|
Label: |
hvg_runtimes_data_v2.zip |
|
Text: |
Version 2 |
|
Notes: |
application/zip |
|
Label: |
hvg_runtimes_data.zip |
|
Notes: |
application/zip |