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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 56676

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Dr. Ebrahim Ghaderpour

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Department of Earth Sciences & CERI Research Centre, Sapienza University of Rome, P.le Aldo Moro, 5, 00185 Rome, Italy
Interests: artificial intelligence; big data analytics; geology, hydrology; remote sensing; time series analysis
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Prof. Dr. Quazi K. Hassan

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Department of Geomatics Engineering, Schulich School of Engineering, University of Calgary, 2500 University Dr. N.W., Calgary, AB T2N 1N4, Canada
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
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Prof. Dr. Spiros Pagiatakis

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Lassonde School of Engineering, York University, Toronto, ON, Canada
Interests: geodesy; geodynamics; gravity field; gravity space missions; signals and systems; data analytics
Special Issues, Collections and Topics in MDPI journals

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

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

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Keywords

time series analysis
wavelet analysis
forecasting
trend analysis
monitoring
regularization
non-stationarity

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Published Papers (13 papers)

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Research

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20 pages, 4798 KiB

Open AccessArticle

Characterizing Cold Days and Spells and Their Relationship with Cold-Related Mortality in Bangladesh

by Md. Mahbub Alam, A. S. M. Mahtab, M. Razu Ahmed and Quazi K. Hassan

Sensors 2023, 23(5), 2832; https://doi.org/10.3390/s23052832 - 5 Mar 2023

Cited by 1 | Viewed by 2086

Abstract

This research examined the characteristics of cold days and spells in Bangladesh using long-term averages (1971–2000) of maximum (T_max) and minimum temperatures (T_min) and their standard deviations (SD). Cold days and spells were calculated and their rate of change during the winter months (December–February) of 2000–2021 was quantified. In this research, a cold day was defined as when the daily maximum or minimum temperature is ≤−1.5 the standard deviations of the long-term daily average of maximum or minimum temperature and the daily average air temperature was equal to or below 17 °C. The results showed that the cold days were more in the west-northwestern regions and far less in the southern and southeastern regions. A gradual decrease in cold days and spells was found from the north and northwest towards the south and southeast. The highest number of cold spells (3.05 spells/year) was experienced in the northwest Rajshahi division and the lowest (1.70 spells/year) in the northeast Sylhet division. In general, the number of cold spells was found to be much higher in January than in the other two winter months. In the case of cold spell severity, Rangpur and Rajshahi divisions in the northwest experienced the highest number of extreme cold spells against the highest number of mild cold spells in the Barishal and Chattogram divisions in the south and southeast. While nine (out of twenty-nine) weather stations in the country showed significant trends in cold days in December, it was not significant on the seasonal scale. Adapting the proposed method would be useful in calculating cold days and spells to facilitate regional-focused mitigation and adaptation to minimize cold-related deaths. Full article

(This article belongs to the Special Issue Advances in Time Series Analysis)

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15 pages, 3292 KiB

Open AccessArticle

Binned Data Provide Better Imputation of Missing Time Series Data from Wearables

by Shweta Chakrabarti, Nupur Biswas, Khushi Karnani, Vijay Padul, Lawrence D. Jones, Santosh Kesari and Shashaanka Ashili

Sensors 2023, 23(3), 1454; https://doi.org/10.3390/s23031454 - 28 Jan 2023

Cited by 9 | Viewed by 4021

Abstract

The presence of missing values in a time-series dataset is a very common and well-known problem. Various statistical and machine learning methods have been developed to overcome this problem, with the aim of filling in the missing values in the data. However, the performances of these methods vary widely, showing a high dependence on the type of data and correlations within the data. In our study, we performed some of the well-known imputation methods, such as expectation maximization, k-nearest neighbor, iterative imputer, random forest, and simple imputer, to impute missing data obtained from smart, wearable health trackers. In this manuscript, we proposed the use of data binning for imputation. We showed that the use of data binned around the missing time interval provides a better imputation than the use of a whole dataset. Imputation was performed for 15 min and 1 h of continuous missing data. We used a dataset with different bin sizes, such as 15 min, 30 min, 45 min, and 1 h, and we carried out evaluations using root mean square error (RMSE) values. We observed that the expectation maximization algorithm worked best for the use of binned data. This was followed by the simple imputer, iterative imputer, and k-nearest neighbor, whereas the random forest method had no effect on data binning during imputation. Moreover, the smallest bin sizes of 15 min and 1 h were observed to provide the lowest RMSE values for the majority of the time frames during the imputation of 15 min and 1 h of missing data, respectively. Although applicable to digital health data, we think that this method will also find applicability in other domains. Full article

(This article belongs to the Special Issue Advances in Time Series Analysis)

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18 pages, 55668 KiB

Open AccessArticle

State Causality and Adaptive Covariance Decomposition Based Time Series Forecasting

by Jince Wang, Zibo He, Tianyu Geng, Feihu Huang, Pu Gong, Peiyu Yi and Jian Peng

Sensors 2023, 23(2), 809; https://doi.org/10.3390/s23020809 - 10 Jan 2023

Cited by 1 | Viewed by 2689

Abstract

Time series forecasting is a very vital research topic. The scale of time series in numerous industries has risen considerably in recent years as a result of the advancement of information technology. However, the existing algorithms pay little attention to generating large-scale time series. This article designs a state causality and adaptive covariance decomposition-based time series forecasting method (SCACD). As an observation sequence, the majority of time series is generated under the influence of hidden states. First, SCACD builds neural networks to adaptively estimate the mean and covariance matrix of latent variables; Then, SCACD employs causal convolution to forecast the distribution of future latent variables; Lastly, to avoid loss of information, SCACD applies a sampling approach based on Cholesky decomposition to generate latent variables and observation sequences. Compared to existing outstanding time series prediction models on six real datasets, the model can achieve long-term forecasting while also being lighter, and the forecasting accuracy is improved in the great majority of the prediction tasks. Full article

(This article belongs to the Special Issue Advances in Time Series Analysis)

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19 pages, 90091 KiB

Open AccessArticle

3-D Data Interpolation and Denoising by an Adaptive Weighting Rank-Reduction Method Using Multichannel Singular Spectrum Analysis Algorithm

by Farzaneh Bayati and Daniel Trad

Sensors 2023, 23(2), 577; https://doi.org/10.3390/s23020577 - 4 Jan 2023

Cited by 13 | Viewed by 2249

Abstract

Addressing insufficient and irregular sampling is a difficult challenge in seismic processing and imaging. Recently, rank reduction methods have become popular in seismic processing algorithms for simultaneous denoising and interpolating. These methods are based on rank reduction of the trajectory matrices using truncated singular value decomposition (TSVD). Estimation of the ranks of these trajectory matrices depends on the number of plane waves in the processing window; however, for the more complicated data, the rank reduction method may fail or give poor results. In this paper, we propose an adaptive weighted rank reduction (AWRR) method that selects the optimum rank in each window automatically. The method finds the maximum ratio of the energy between two singular values. The AWRR method selects a large rank for the highly curved complex events, which leads to remaining residual errors. To overcome the residual errors, a weighting operator on the selected singular values minimizes the effect of noise projection on the signal projection. We tested the efficiency of the proposed method by applying it to both synthetic and real seismic data. Full article

(This article belongs to the Special Issue Advances in Time Series Analysis)

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20 pages, 33429 KiB

Open AccessArticle

Urban DAS Data Processing and Its Preliminary Application to City Traffic Monitoring

by Hang Wang, Yunfeng Chen, Rui Min and Yangkang Chen

Sensors 2022, 22(24), 9976; https://doi.org/10.3390/s22249976 - 18 Dec 2022

Cited by 22 | Viewed by 3793

Abstract

Distributed acoustic sensing (DAS) is an emerging technology for recording vibration signals via the optical fibers buried in subsurface conduits. Its relatively easy-to-deploy and high spatial and temporal sampling characteristics make DAS an appealing tool to record seismic wavefields at higher quantity and quality than traditional geophones. Considering that the usage of optical fibers in the urban environment has drawn relatively less attention aside from its functionality as a telecommunication cable, we examine its ability to record seismic signals and investigate its preliminary application in city traffic monitoring. To solve the problems that DAS signals are prone to a variety of environmental noise and are generally of weak amplitude compared to noise, we propose a fast workflow for real-time DAS data processing, which can enhance the detection of regular car signals and suppress the other components. We conduct a DAS experiment in Hangzhou, China, a typical metropolitan area that can provide us with a rich data library to validate our DAS data-processing workflow. The well-processed data enable us to extract their slope and coherency attributes that can provide an estimate of real traffic situations. The one-minute (with video validations) and 24 h statistics of these attributes show that the speed and volume of car flow are well correlated demonstrates the robustness of the proposed data processing workflow and great potential of DAS for city traffic monitoring with high precision and convenience. However, challenges also exist in view that all the attributes are statistically analyzed based on the behaviors of a large number of cars, which is meaningful but lacking in precision. Therefore, we suggest developing more quantitative processing and analyzing methods to provide precise information on individual cars in future works. Full article

(This article belongs to the Special Issue Advances in Time Series Analysis)

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29 pages, 2785 KiB

Open AccessArticle

Using Multi-Dimensional Dynamic Time Warping to Identify Time-Varying Lead-Lag Relationships

by Johannes Stübinger and Dominik Walter

Sensors 2022, 22(18), 6884; https://doi.org/10.3390/s22186884 - 12 Sep 2022

Cited by 11 | Viewed by 6969

Abstract

This paper develops a multi-dimensional Dynamic Time Warping (DTW) algorithm to identify varying lead-lag relationships between two different time series. Specifically, this manuscript contributes to the literature by improving upon the use towards lead-lag estimation. Our two-step procedure computes the multi-dimensional DTW alignment with the aid of shapeDTW and then utilises the output to extract the estimated time-varying lead-lag relationship between the original time series. Next, our extensive simulation study analyses the performance of the algorithm compared to the state-of-the-art methods Thermal Optimal Path (TOP), Symmetric Thermal Optimal Path (TOPS), Rolling Cross-Correlation (RCC), Dynamic Time Warping (DTW), and Derivative Dynamic Time Warping (DDTW). We observe a strong outperformance of the algorithm regarding efficiency, robustness, and feasibility. Full article

(This article belongs to the Special Issue Advances in Time Series Analysis)

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18 pages, 3965 KiB

Open AccessArticle

Evaluation of Three Feature Dimension Reduction Techniques for Machine Learning-Based Crop Yield Prediction Models

by Hoa Thi Pham, Joseph Awange and Michael Kuhn

Sensors 2022, 22(17), 6609; https://doi.org/10.3390/s22176609 - 1 Sep 2022

Cited by 20 | Viewed by 4597

Abstract

Machine learning (ML) has been widely used worldwide to develop crop yield forecasting models. However, it is still challenging to identify the most critical features from a dataset. Although either feature selection (FS) or feature extraction (FX) techniques have been employed, no research compares their performances and, more importantly, the benefits of combining both methods. Therefore, this paper proposes a framework that uses non-feature reduction (All-F) as a baseline to investigate the performance of FS, FX, and a combination of both (FSX). The case study employs the vegetation condition index (VCI)/temperature condition index (TCI) to develop 21 rice yield forecasting models for eight sub-regions in Vietnam based on ML methods, namely linear, support vector machine (SVM), decision tree (Tree), artificial neural network (ANN), and Ensemble. The results reveal that FSX takes full advantage of the FS and FX, leading FSX-based models to perform the best in 18 out of 21 models, while 2 (1) for FS-based (FX-based) models. These FXS-, FS-, and FX-based models improve All-F-based models at an average level of 21% and up to 60% in terms of RMSE. Furthermore, 21 of the best models are developed based on Ensemble (13 models), Tree (6 models), linear (1 model), and ANN (1 model). These findings highlight the significant role of FS, FX, and specially FSX coupled with a wide range of ML algorithms (especially Ensemble) for enhancing the accuracy of predicting crop yield. Full article

(This article belongs to the Special Issue Advances in Time Series Analysis)

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16 pages, 3597 KiB

Open AccessArticle

A Remote Sensing-Based Analysis of the Impact of Syrian Crisis on Agricultural Land Abandonment in Yarmouk River Basin

by Khaled Hazaymeh, Wahib Sahwan, Sattam Al Shogoor and Brigitta Schütt

Sensors 2022, 22(10), 3931; https://doi.org/10.3390/s22103931 - 23 May 2022

Cited by 14 | Viewed by 3007

Abstract

In this study, we implemented a remote sensing-based approach for monitoring abandoned agricultural land in the Yarmouk River Basin (YRB) in Southern Syria and Northern Jordan during the Syrian crisis. A time series analysis for the Normalized Difference Vegetation Index (NDVI) and Normalized Difference Moisture Index (NDMI) was conducted using 1650 multi-temporal images from Landsat-5 and Landsat-8 between 1986 and 2021. We analyzed the agricultural phenological profiles and investigated the impact of the Syrian crisis on agricultural activities in YRB. The analysis was performed using JavaScript commands in Google Earth Engine. The results confirmed the impact of the Syrian crisis on agricultural land use. The phenological characteristics of NDVI and NDMI during the crisis (2013–2021) were compared to the phenological profiles for the period before the crisis (1986–2010). The NDVI and NDMI profiles had smooth, bell-shaped, and single beak NDVI and NDMI values during the period of crisis in comparison to those irregular phenological profiles for the period before the crisis or during the de-escalation/reconciliation period in the study area. The maximum average NDVI and NDMI values was found in March during the crisis, indicating the progress of natural vegetation and fallow land, while they fluctuated between March and April before the crisis or during the de-escalation/reconciliation period, indicating regular agricultural and cultivation practices. Full article

(This article belongs to the Special Issue Advances in Time Series Analysis)

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13 pages, 963 KiB

Open AccessArticle

A Hidden Markov Ensemble Algorithm Design for Time Series Analysis

by Ting Lin, Miao Wang, Min Yang and Xu Yang

Sensors 2022, 22(8), 2950; https://doi.org/10.3390/s22082950 - 12 Apr 2022

Cited by 2 | Viewed by 2614

Abstract

With the exponential growth of data, solving classification or regression tasks by mining time series data has become a research hotspot. Commonly used methods include machine learning, artificial neural networks, and so on. However, these methods only extract the continuous or discrete features of sequences, which have the drawbacks of low information utilization, poor robustness, and computational complexity. To solve these problems, this paper innovatively uses Wasserstein distance instead of Kullback–Leibler divergence and uses it to construct an autoencoder to learn discrete features of time series. Then, a hidden Markov model is used to learn the continuous features of the sequence. Finally, stacking is used to ensemble the two models to obtain the final model. This paper experimentally verifies that the ensemble model has lower computational complexity and is close to state-of-the-art classification accuracy. Full article

(This article belongs to the Special Issue Advances in Time Series Analysis)

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21 pages, 2916 KiB

Open AccessArticle

Automatic Muscle Artifacts Identification and Removal from Single-Channel EEG Using Wavelet Transform with Meta-Heuristically Optimized Non-Local Means Filter

by Souvik Phadikar, Nidul Sinha, Rajdeep Ghosh and Ebrahim Ghaderpour

Sensors 2022, 22(8), 2948; https://doi.org/10.3390/s22082948 - 12 Apr 2022

Cited by 35 | Viewed by 4745

Abstract

Electroencephalogram (EEG) signals may get easily contaminated by muscle artifacts, which may lead to wrong interpretation in the brain–computer interface (BCI) system as well as in various medical diagnoses. The main objective of this paper is to remove muscle artifacts without distorting the information contained in the EEG. A novel multi-stage EEG denoising method is proposed for the first time in which wavelet packet decomposition (WPD) is combined with a modified non-local means (NLM) algorithm. At first, the artifact EEG signal is identified through a pre-trained classifier. Next, the identified EEG signal is decomposed into wavelet coefficients and corrected through a modified NLM filter. Finally, the artifact-free EEG is reconstructed from corrected wavelet coefficients through inverse WPD. To optimize the filter parameters, two meta-heuristic algorithms are used in this paper for the first time. The proposed system is first validated on simulated EEG data and then tested on real EEG data. The proposed approach achieved average mutual information (MI) as 2.9684 ± 0.7045 on real EEG data. The result reveals that the proposed system outperforms recently developed denoising techniques with higher average MI, which indicates that the proposed approach is better in terms of quality of reconstruction and is fully automatic. Full article

(This article belongs to the Special Issue Advances in Time Series Analysis)

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17 pages, 982 KiB

Open AccessArticle

Automated Feature Extraction on AsMap for Emotion Classification Using EEG

by Md. Zaved Iqubal Ahmed, Nidul Sinha, Souvik Phadikar and Ebrahim Ghaderpour

Sensors 2022, 22(6), 2346; https://doi.org/10.3390/s22062346 - 18 Mar 2022

Cited by 59 | Viewed by 5167

Abstract

Emotion recognition using EEG has been widely studied to address the challenges associated with affective computing. Using manual feature extraction methods on EEG signals results in sub-optimal performance by the learning models. With the advancements in deep learning as a tool for automated feature engineering, in this work, a hybrid of manual and automatic feature extraction methods has been proposed. The asymmetry in different brain regions is captured in a 2D vector, termed the AsMap, from the differential entropy features of EEG signals. These AsMaps are then used to extract features automatically using a convolutional neural network model. The proposed feature extraction method has been compared with differential entropy and other feature extraction methods such as relative asymmetry, differential asymmetry and differential caudality. Experiments are conducted using the SJTU emotion EEG dataset and the DEAP dataset on different classification problems based on the number of classes. Results obtained indicate that the proposed method of feature extraction results in higher classification accuracy, outperforming the other feature extraction methods. The highest classification accuracy of 97.10% is achieved on a three-class classification problem using the SJTU emotion EEG dataset. Further, this work has also assessed the impact of window size on classification accuracy. Full article

(This article belongs to the Special Issue Advances in Time Series Analysis)

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23 pages, 5148 KiB

Open AccessArticle

Temporal Monitoring and Predicting of the Abundance of Malaria Vectors Using Time Series Analysis of Remote Sensing Data through Google Earth Engine

by Fahimeh Youssefi, Mohammad Javad Valadan Zoej, Ahmad Ali Hanafi-Bojd, Alireza Borhani Dariane, Mehdi Khaki, Alireza Safdarinezhad and Ebrahim Ghaderpour

Sensors 2022, 22(5), 1942; https://doi.org/10.3390/s22051942 - 2 Mar 2022

Cited by 15 | Viewed by 3431

Abstract

In many studies regarding the field of malaria, environmental factors have been acquired in single-time, multi-time or a short-time series using remote sensing and meteorological data. Selecting the best periods of the year to monitor the habitats of Anopheles larvae can be effective in better and faster control of malaria outbreaks. In this article, high-risk times for three regions in Iran, including Qaleh-Ganj, Sarbaz and Bashagard counties with a history of malaria prevalence were estimated. For this purpose, a series of environmental factors affecting the growth and survival of Anopheles were used over a seven-year period through the Google Earth Engine. The results of this study indicated two high-risk times for Qaleh-Ganj and Bashagard counties and three high-risk times for Sarbaz county over the course of a year observing an increase in the abundance of Anopheles mosquitoes. Further evaluation of the results against the entomological data available in previous studies showed that the high-risk times predicted in this study were consistent with an increase in the abundance of Anopheles mosquitoes in the study areas. The proposed method is extremely useful for temporal prediction of the increase in abundance of Anopheles mosquitoes in addition to the use of optimal data aimed at monitoring the exact location of Anopheles habitats. Full article

(This article belongs to the Special Issue Advances in Time Series Analysis)

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29 pages, 2207 KiB

Open AccessReview

Deep Unsupervised Domain Adaptation with Time Series Sensor Data: A Survey

by Yongjie Shi, Xianghua Ying and Jinfa Yang

Sensors 2022, 22(15), 5507; https://doi.org/10.3390/s22155507 - 23 Jul 2022

Cited by 39 | Viewed by 7963

Abstract

Sensors are devices that output signals for sensing physical phenomena and are widely used in all aspects of our social production activities. The continuous recording of physical parameters allows effective analysis of the operational status of the monitored system and prediction of unknown risks. Thanks to the development of deep learning, the ability to analyze temporal signals collected by sensors has been greatly improved. However, models trained in the source domain do not perform well in the target domain due to the presence of domain gaps. In recent years, many researchers have used deep unsupervised domain adaptation techniques to address the domain gap between signals collected by sensors in different scenarios, i.e., using labeled data in the source domain and unlabeled data in the target domain to improve the performance of models in the target domain. This survey first summarizes the background of recent research on unsupervised domain adaptation with time series sensor data, the types of sensors used, the domain gap between the source and target domains, and commonly used datasets. Then, the paper classifies and compares different unsupervised domain adaptation methods according to the way of adaptation and summarizes different adaptation settings based on the number of source and target domains. Finally, this survey discusses the challenges of the current research and provides an outlook on future work. This survey systematically reviews and summarizes recent research on unsupervised domain adaptation for time series sensor data to provide the reader with a systematic understanding of the field. Full article

(This article belongs to the Special Issue Advances in Time Series Analysis)

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