"Time Series Analysis: New Insights" ed. by Rifaat Abdalla, Mohammed El-Diasty, Andrey Kostogryzov, Nikolay Makhutov

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.

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.

Contents
1. Sensitivity Analysis and Modeling for DEM Errors
2. ARIMA Models with Time-Dependent Coefficients: Official Statistics Examples
3. Methods of Conditionally Optimal Forecasting for Stochastic Synergetic CALS Technologies
4. Probabilistic Predictive Modelling for Complex System Risk Assessments
5. A New Approach of Power Transformations in Functional Non-Parametric Temperature Time Series
6. Change Detection by Monitoring Residuals from Time Series Models
7. Comparison of the Out-of-Sample Forecast for Inflation Rates in Nigeria Using ARIMA and ARIMAX Models
8. The L2 – Structure of Subordinated Solution of Continuous-Time Bilinear Time Series


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