Special Issue "Advances in Time Series Analysis"
A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Environmental Sensing".
Deadline for manuscript submissions: closed (31 December 2022) | Viewed by 15214
Special Issue Editors

Interests: artificial intelligence; big data analytics; remote sensing; hydrology; climate change; geoscience
Special Issues, Collections and Topics in MDPI journals

Interests: optical/thermal remote sensing in: (i) forecasting and monitoring of natural hazards/disasters, such as forest fire, drought, and flooding; (ii) comprehending the dynamics of natural resources, such as forestry, agriculture, and water; (iii) modelling issues related to boreal environment
Special Issues, Collections and Topics in MDPI journals

Interests: geodesy; geodynamics; gravity field from terrestrial and space platforms; atmospheric studies using GNSS and Low Earth Orbiters; space gravity missions and ionospheric dynamics; data analytics
Special Issue Information
Dear Colleagues,
Time series analysis has recently attracted wide attention in many fields of science, such as remote sensing, hydrology, geodesy, geophysics, astronomy, finance, and medicine. Time series analysis is a very challenging task and often requires pre-knowledge of the data. For example, time series obtained from Earth observation data are often unevenly sampled (equally spaced) and have uncertainties due to various reasons, such as sensor defects and atmospheric effects. Therefore, new techniques that can consider such uncertainties, as well as irregularities in sampling, are highly demanded.
There are many time series analysis techniques proposed for various purposes, such as trend estimation, breakpoint detection, forecasting, monitoring, and regularization—e.g., spectral and wavelet methods, such as Fourier transform (FT), least-squares spectral analysis (LSSA), continuous wavelet transform (CWT), weighted wavelet Z-transform (WWZ), and least-squares wavelet analysis (LSWA); breakpoint detection methods, such as breaks for additive seasonal and trend (BFAST), continuous change detection and classification (CCDC), detecting breakpoints and estimating segments in trend (DBEST), and jumps upon spectrum and trend (JUST); trend analysis methods, such as linear regression, season-trend fit, and Mann–Kendall analysis; and forecasting methods, such as moving average (MA), and autoregressive integrated moving average (ARIMA), long short-time memory (LSTM), and many more.
In this Special Issue, we welcome:
1) Manuscripts describing applications of the methods mentioned above for analyzing time series obtained from various sensors;
2) Manuscripts demonstrating new time series analysis techniques and/or applications of existing methods.
Dr. Ebrahim Ghaderpour
Prof. Dr. Quazi K. Hassan
Prof. Dr. Spiros Pagiatakis
Guest Editors
Manuscript Submission Information
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Keywords
- time series analysis
- wavelet analysis
- forecasting
- trend analysis
- monitoring
- regularization
- non-stationarity