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This book includes contributions from researchers, scholars, and professionals about the most recent theory, models, and applications for interdisciplinary and multidisciplinary research encircling disciplines of computer science, mathematics, statistics, geography, and more in time series analysis and forecasting/backcasting.
Time series analysis describes, explains, and predicts changes in a phenomenon through time. People have utilized techniques that add a distinctive spatial dimension to this type of analysis. Major applications of spatiotemporal analysis include forecasting epidemics, analyzing the development of traffic conditions in urban networks, and forecasting/backcasting economic risks such as those associated with changing house prices and the occurrence of hazardous events.
Time Series Analysis
Time Series Classification: A Review of Algorithms and Implementations
A Heartbeat-Interval Time Series Analysis, Modified Detrended Fluctuation Analysis, mDFA, Distinguishes between Stressed- and Happy-Heartbeats: From Invertebrate Animals to Humans
Use of the Gini Coefficient for the Analysis of Heart Rate Variability in Sick and Healthy Individuals
Handling Missing Data in the Time-Series Data from Wearables
Variability Analysis of Observational Time Series: An Overview of the Decomposition Methods for Non-Stationary and Noisy Signals
Segmentation of High-Dimensional Matrix-Variate Time Series
Neural Networks in Nonlinear Time Series: A Subsampling Model Selection Procedure
Analysis and Modeling of Rainfed Crops Dynamics Based on NDVI Time Series in Central Spain
Momentum Periods of Feedback Trading toward Exchange Rate Volatility in ASEAN Countries
Electric Fault Diagnosis in Induction Machines Using Motor Current Signature Analysis (MCSA)
A Comparative Analysis of Image Encoding of Time Series for Anomaly Detection
Fractal Dynamics and Fibonacci Sequences: A Time Series Analysis of Cultural Attractor Landscapes