DATA ANALYSIS | Time Series Analysis

2003 ◽  
pp. 621-624
Author(s):  
G.R. North
Keyword(s):  
Time Series ◽  
Data Analysis ◽  
Time Series Analysis ◽  
Analysis Time ◽  
Series Analysis

Time Series

2021 ◽  
pp. 288-291
Author(s):  
Jean-Frédéric Morin ◽  
Christian Olsson ◽  
Ece Özlem Atikcan
Keyword(s):  
Time Series ◽  
Social Change ◽  
Time Series Analysis ◽  
Longitudinal Analysis ◽  
Analysis Time ◽  
Social Phenomena ◽  
Series Analysis ◽  
Level Of Analysis ◽  
Patterns Of Recurrence ◽  
Over Time

This chapter focuses on time series analysis, a statistical method of longitudinal analysis which is suitable if researchers are interested in the temporality of social phenomena and want to analyse social change and patterns of recurrence over time. In contrast to other statistical methods of longitudinal analysis, time series analysis can be applied even if researchers have only a few cases (maybe even only one) and only a few (maybe even only one) variables. Time series can be built for any level of analysis, as cases can be persons, but are usually organizations or countries. In order to build a time series, the variables need to have been measured several times over a given period, and for each measurement one needs to know the measurement date. There are different goals when doing time series analysis, which can be used in descriptive, explanatory, and interpretive approaches.

Introduction to Analysis Using Time Components

2010 ◽  
pp. 154-192
Author(s):  
Patricia Cerrito ◽  
John Cerrito
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Fixed Time ◽  
Analysis Time ◽  
Time Intervals ◽  
Time Points ◽  
Series Analysis ◽  
Time Component ◽  
Time Stamps ◽  
Insurance Data

The introduction of a time component requires the use of statistical methods that can utilize dependent data. The assumption of independence that is required for regression models is no longer applicable. In this section, we will work with time series analysis. Time series analysis requires that data are collected at discrete, fixed time intervals. Observational and insurance data contain time stamps as to the date of service. These time stamps are transactional in nature and do not occur at fixed time intervals. Therefore, the first step in such an analysis is to convert the transactional time points into fixed time intervals. We need to decide upon the interval: every minute, hour, day, week, month, year. The specific interval will depend upon the analysis to be performed. Once that is completed, the standard time series analysis methods can be used. As an example, we use the MEPS dataset for medications. We use the date of January 1 as time zero.

scholarly journals Satellite Image Time Series Analysis for Big Earth Observation Data

2021 ◽  
Vol 13 (13) ◽  
pp. 2428
Author(s):  
Rolf Simoes ◽  
Gilberto Camara ◽  
Gilberto Queiroz ◽  
Felipe Souza ◽  
Pedro R. Andrade ◽  
...  
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Data Analysis ◽  
Time Series Analysis ◽  
Satellite Image ◽  
Earth Observation ◽  
Observation Data ◽  
Accuracy Measurement ◽  
Series Analysis ◽  
Earth Observation Data

The development of analytical software for big Earth observation data faces several challenges. Designers need to balance between conflicting factors. Solutions that are efficient for specific hardware architectures can not be used in other environments. Packages that work on generic hardware and open standards will not have the same performance as dedicated solutions. Software that assumes that its users are computer programmers are flexible but may be difficult to learn for a wide audience. This paper describes sits, an open-source R package for satellite image time series analysis using machine learning. To allow experts to use satellite imagery to the fullest extent, sits adopts a time-first, space-later approach. It supports the complete cycle of data analysis for land classification. Its API provides a simple but powerful set of functions. The software works in different cloud computing environments. Satellite image time series are input to machine learning classifiers, and the results are post-processed using spatial smoothing. Since machine learning methods need accurate training data, sits includes methods for quality assessment of training samples. The software also provides methods for validation and accuracy measurement. The package thus comprises a production environment for big EO data analysis. We show that this approach produces high accuracy for land use and land cover maps through a case study in the Cerrado biome, one of the world’s fast moving agricultural frontiers for the year 2018.

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