Open AccessBook
Forecasting, Structural Time Series Models and the Kalman Filter
TLDR
In this article, the Kalman filter and state space models were used for univariate structural time series models to estimate, predict, and smoothen the univariate time series model.Abstract:
List of figures Acknowledgement Preface Notation and conventions List of abbreviations 1. Introduction 2. Univariate time series models 3. State space models and the Kalman filter 4. Estimation, prediction and smoothing for univariate structural time series models 5. Testing and model selection 6. Extensions of the univariate model 7. Explanatory variables 8. Multivariate models 9. Continuous time Appendices Selected answers to exercises References Author index Subject index.read more
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Testing the null hypothesis of stationarity against the alternative of a unit root: How sure are we that economic time series have a unit root?
TL;DR: In this paper, a test of the null hypothesis that an observable series is stationary around a deterministic trend is proposed, where the series is expressed as the sum of deterministic trends, random walks, and stationary error.
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Time Series Analysis
TL;DR: This paper provides a concise overview of time series analysis in the time and frequency domains with lots of references for further reading.
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Random early detection gateways for congestion avoidance
Sally Floyd,Van Jacobson +1 more
TL;DR: Red gateways are designed to accompany a transport-layer congestion control protocol such as TCP and have no bias against bursty traffic and avoids the global synchronization of many connections decreasing their window at the same time.
Journal ArticleDOI
A protocol for data exploration to avoid common statistical problems
TL;DR: A protocol for data exploration is provided; current tools to detect outliers, heterogeneity of variance, collinearity, dependence of observations, problems with interactions, double zeros in multivariate analysis, zero inflation in generalized linear modelling, and the correct type of relationships between dependent and independent variables are discussed; and advice on how to address these problems when they arise is provided.
Book
Introduction to time series and forecasting
TL;DR: In this paper, the authors present a general approach to time series analysis based on simple time series models and the Autocorrelation Function (AFF) and the Wold Decomposition.