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Fitting Functions to Time Series
The purpose of this chapter is to establish a foundation for time-series analysis of remotely sensed data, which is typically arranged as an ordered...
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Aggregating Images for Time Series
Many remote sensing datasets consist of repeated observations over time. The interval between observations can vary widely. Many applications,...
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Time series outlier removal and imputing methods based on Colombian weather stations data
The time data series of weather stations are a source of information for floods. The study of the previous wintertime series allows knowing the...
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Filtering Outliers in GNSS Time Series Data in Real-Time Bridge Monitoring
Real-time GNSS data plays a crucial role in structural health monitoring and early warning in bridge displacement monitoring. Outliers in the range...
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Monitoring the risk of a tailings dam collapse through spectral analysis of satellite InSAR time-series data
Slope failures possess destructive power that can cause significant damage to both life and infrastructure. Monitoring slopes prone to instabilities...
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Interpreting Time Series with CCDC
Continuous change detection and classification (CCDC) is a land change monitoring algorithm designed to operate on time series of satellite data,...
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Data-driven adaptive GM(1,1) time series prediction model for thermal comfort
In this paper, the future prediction of predicted mean vote (PMV) index of indoor environment is studied. PMV is the evaluation index used in this...
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Exploring Lagged Effects in Time Series
In this chapter, we will introduce lagged effects to build on the previous work in modeling time series data. Time-lagged effects occur when an event...
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An IoT framework for quality analysis of aquatic water data using time-series convolutional neural network
Water quality monitoring and analysis in fish farms are of paramount importance for the aquaculture sector; however, traditional methods can pose...
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Modeling noisy time-series data of crime with stochastic differential equations
We develop and calibrate stochastic continuous models that capture crime dynamics in the city of Valencia, Spain. From the emergency phone, data...
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Interpreting Annual Time Series with LandTrendr
Time-series analysis of change can be achieved by fitting the entire spectral trajectory using simple statistical models. These allow us to both...
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Quantifying forest resilience post forest fire disturbances using time-series satellite data
Quantification of forest resilience will help us to manage the sustainability of the forest environment and the safety of biodiversity. Measuring...
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Temporal and spatial analysis of vegetation cover change in the Yellow River Delta based on Landsat and MODIS time series data
Based on the Landsat normalized difference vegetation index (NDVI) and the NDVI product of MODIS, this study synthesized two kinds of time-series...
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Selection of statistical technique for imputation of single site-univariate and multisite–multivariate methods for particulate pollutants time series data with long gaps and high missing percentage
Monitoring air contaminants has become essential to exposure science, toxicology, and public health research. However, missing values are common...
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An MDS-based unifying approach to time series K-means clustering: application in the dynamic time war** framework
Partitioning algorithms, and in particular K-means clustering, are widely used in time series analysis. K-means clustering is intrinsically related...
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A Big Data Solution to Predict Cryptocurrency Market Trends: A Time-Series Machine Learning Approach
CryptocurrencyCryptocurrency trend analysis allows researchers to study cryptocurrencyCryptocurrency market behaviour and propose predictive...
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Nonlinear GARCH-type models for ordinal time series
Despite their relevance in various areas of application, only few stochastic models for ordinal time series are discussed in the literature. To allow...
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Time series–based prediction of antibiotic degradation via photocatalysis using ensemble gradient boosting
This study aims to evaluate the effectiveness of the laboratory-made catalyst Ni 2 P–ZrO 2 (NPZ) in the degradation of an antibiotic in an aqueous...
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MSTL-NNAR: a new hybrid model of machine learning and time series decomposition for wind speed forecasting
Wind speed forecasting is essential for various domains, such as renewable energy generation, aviation, agriculture, and disaster management....
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Modeling leaf area index using time-series remote sensing and topographic data in pure Anatolian black pine stands
We aimed to map and analyze LAI by using Landsat 8 and Sentinel-2 time series and the corresponding ground measurements collected in pure Anatolian...
