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Time series data consist of a collection of observations obtained through repeated measurements over time. When the points are plotted on a graph, one of the axes is always time. Time series analysis is a specific way of analyzing a sequence of data points. Time series data are everywhere since time is a constituent of everything that is observable. As our world becomes increasingly digitized, sensors and systems are constantly emitting a relentless stream of time series data, which has numerous applications across various industries. The editors of this book are happy to provide the specialized reader community with this book as a modest contribution to this rapidly developing domain.
Sensitivity Analysis and Modeling for DEM Errors
ARIMA Models with Time-Dependent Coefficients: Official Statistics Examples
Methods of Conditionally Optimal Forecasting for Stochastic Synergetic CALS Technologies
Probabilistic Predictive Modelling for Complex System Risk Assessments
A New Approach of Power Transformations in Functional Non-Parametric Temperature Time Series
Change Detection by Monitoring Residuals from Time Series Models
Comparison of the Out-of-Sample Forecast for Inflation Rates in Nigeria Using ARIMA and ARIMAX Models
The L2 – Structure of Subordinated Solution of Continuous-Time Bilinear Time Series