Time Series Analysis of Open Source Projects Popularity

Author(s):  
Shahab Bayati ◽  
Marzieh Heidary
2020 ◽  
Vol 12 (3) ◽  
pp. 424 ◽  
Author(s):  
Yu Morishita ◽  
Milan Lazecky ◽  
Tim Wright ◽  
Jonathan Weiss ◽  
John Elliott ◽  
...  

For the past five years, the 2-satellite Sentinel-1 constellation has provided abundant and useful Synthetic Aperture Radar (SAR) data, which have the potential to reveal global ground surface deformation at high spatial and temporal resolutions. However, for most users, fully exploiting the large amount of associated data is challenging, especially over wide areas. To help address this challenge, we have developed LiCSBAS, an open-source SAR interferometry (InSAR) time series analysis package that integrates with the automated Sentinel-1 InSAR processor (LiCSAR). LiCSBAS utilizes freely available LiCSAR products, and users can save processing time and disk space while obtaining the results of InSAR time series analysis. In the LiCSBAS processing scheme, interferograms with many unwrapping errors are automatically identified by loop closure and removed. Reliable time series and velocities are derived with the aid of masking using several noise indices. The easy implementation of atmospheric corrections to reduce noise is achieved with the Generic Atmospheric Correction Online Service for InSAR (GACOS). Using case studies in southern Tohoku and the Echigo Plain, Japan, we demonstrate that LiCSBAS applied to LiCSAR products can detect both large-scale (>100 km) and localized (~km) relative displacements with an accuracy of <1 cm/epoch and ~2 mm/yr. We detect displacements with different temporal characteristics, including linear, periodic, and episodic, in Niigata, Ojiya, and Sanjo City, respectively. LiCSBAS and LiCSAR products facilitate greater exploitation of globally available and abundant SAR datasets and enhance their applications for scientific research and societal benefit.


2017 ◽  
Author(s):  
Jeffrey Strickland

Time Series Analysis with Open Source Tools introduces the subject using R and Python programming and tools. This book assumes a basic understanding of statistics and mathematical or statistical modeling. Although a little programming experience would be nice, it is not required. There are a few “formulas,” with no theorems or proofs, and calculus never appears. Chapters one and two introduce the topic at hand with an overview and a brief discussion about the components of time series. R programming is introduced in Chapter 3 in the R-Studio environment with decomposing and analyzing the components of time series data using unemployment rate and consumer cost index over time as an example. It also introduces differencing and simple smoothing for making sense of the data and demonstrates the analysis of seasonality using beer sales. It introduces dealing with nonstationary time series data using loans as an example. Finally, it covers an alternative time series analysis method using R with airline passenger data. Chapter 4 introduces Python in the iPython environment for manipulating time series data. It covers working with data to format the time series, displaying and plotting the data, examining trend, and smoothing data using meat data from the U.S. Department of Agriculture. It also introduces loading and formatting data that is not native to Python add-ins. Later chapters cover the various application of time series analysis in several different industries including political, financial, and environmental. ARMA, ARIMA, and UCM methods and covered in detail, and GLARMA models are introduced.


1991 ◽  
Vol 36 (4) ◽  
pp. 349-349
Author(s):  
No authorship indicated

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