02433nam 2200385 450 991064749640332120230328095255.0(CKB)5680000000300311(NjHacI)995680000000300311(EXLCZ)99568000000030031120230328d2023 uy 0engur|||||||||||txtrdacontentcrdamediacrrdacarrierTime Series Analysis New Insights /edited by Rifaat Abdalla [and three others]London :IntechOpen,2023.©20231 online resource (ix, 204 pages) illustrations1-80356-305-2 1-80356-306-0 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.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.Time Series Analysis Time-series analysisTime-series analysis.519.55Abdalla RifaatNjHacINjHaclBOOK9910647496403321Time series analysis257628UNINA