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Record Nr. |
UNINA9910647496403321 |
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Titolo |
Time Series Analysis : New Insights / / edited by Rifaat Abdalla [and three others] |
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Pubbl/distr/stampa |
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London : , : IntechOpen, , 2023 |
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©2023 |
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Descrizione fisica |
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1 online resource (ix, 204 pages) : illustrations |
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Disciplina |
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Soggetti |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di contenuto |
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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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Sommario/riassunto |
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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. |
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