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Publication Date2004, ADVANCED TEXTS IN ECONOMETRICS., ISBN 9780199242030, 162
This book considers periodic time series models for seasonal data, characterized by parameters that differ across the seasons, and focuses on their usefulness...
Economics | Econometric models | Time-series analysis
Economics | Econometric models | Time-series analysis
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International Journal of Forecasting, ISSN 0169-2070, 2006, Volume 22, Issue 3, pp. 443 - 473
We review the past 25 years of research into time series forecasting. In this silver jubilee issue, we naturally highlight results published in journals...
Exponential smoothing | Prediction intervals | Univariate | Count data | Neural nets | Kalman filter | VAR | Seasonality | ARCH | Densities | State space | Long memory | Structural models | Combining | ARIMA | Regime-switching | Nonlinearity | Accuracy measures | Robustness | Transfer function | Multivariate | class eco B | state space | multivariate | prediction intervals | FINANCIAL RISK-MANAGEMENT | structural models | FRACTIONALLY INTEGRATED PROCESSES | count data | STATE-SPACE MODELS | WEIGHTED MOVING AVERAGES | COEFFICIENT AUTOREGRESSIVE MODELS | transfer function | MULTIPLICATIVE HOLT-WINTERS | exponential smoothing | MANAGEMENT | nonlinearity | robustness | combining | ARTIFICIAL NEURAL-NETWORK | univariate | BOOTSTRAP PREDICTION INTERVALS | regime-switching | ECONOMICS | long memory | densities | seasonality | EXPONENTIAL SMOOTHING METHODS | accuracy measures | neural nets | UK MACROECONOMIC VARIABLES | History | Economic forecasting | Research
Exponential smoothing | Prediction intervals | Univariate | Count data | Neural nets | Kalman filter | VAR | Seasonality | ARCH | Densities | State space | Long memory | Structural models | Combining | ARIMA | Regime-switching | Nonlinearity | Accuracy measures | Robustness | Transfer function | Multivariate | class eco B | state space | multivariate | prediction intervals | FINANCIAL RISK-MANAGEMENT | structural models | FRACTIONALLY INTEGRATED PROCESSES | count data | STATE-SPACE MODELS | WEIGHTED MOVING AVERAGES | COEFFICIENT AUTOREGRESSIVE MODELS | transfer function | MULTIPLICATIVE HOLT-WINTERS | exponential smoothing | MANAGEMENT | nonlinearity | robustness | combining | ARTIFICIAL NEURAL-NETWORK | univariate | BOOTSTRAP PREDICTION INTERVALS | regime-switching | ECONOMICS | long memory | densities | seasonality | EXPONENTIAL SMOOTHING METHODS | accuracy measures | neural nets | UK MACROECONOMIC VARIABLES | History | Economic forecasting | Research
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Loading RightsPLoS ONE, ISSN 1932-6203, 2012, Volume 7, Issue 7, p. e41010
Many dynamical systems, including lakes, organisms, ocean circulation patterns, or financial markets, are now thought to have tipping points where critical...
models | desertification | system | leading indicator | catastrophic shifts | ecosystems | climate tipping points | critical slowing-down | regime shifts | states | STATES | REGIME SHIFTS | CATASTROPHIC SHIFTS | ESTIMATORS | STABILITY | MULTIDISCIPLINARY SCIENCES | CRITICAL SLOWING-DOWN | LEADING INDICATOR | DESERTIFICATION | ECOSYSTEMS | VEGETATION | Models, Theoretical | Oceans and Seas | Reproducibility of Results | Models, Statistical | Systems Theory | Ecology - methods | Algorithms | Time Factors | Models, Biological | Computer Simulation | Biology - methods | Lakes | Environment | Diffusion | Usage | Financial markets | Analysis | Methods | Coral reefs | Ocean circulation | Identification methods | Ecosystems | Extinction | Aquatic ecology | Time series | Biomass | Datasets | Environmental science | Water quality | Water circulation | Quality management | Ocean currents | Biodiversity and Ecology | Environmental Sciences
models | desertification | system | leading indicator | catastrophic shifts | ecosystems | climate tipping points | critical slowing-down | regime shifts | states | STATES | REGIME SHIFTS | CATASTROPHIC SHIFTS | ESTIMATORS | STABILITY | MULTIDISCIPLINARY SCIENCES | CRITICAL SLOWING-DOWN | LEADING INDICATOR | DESERTIFICATION | ECOSYSTEMS | VEGETATION | Models, Theoretical | Oceans and Seas | Reproducibility of Results | Models, Statistical | Systems Theory | Ecology - methods | Algorithms | Time Factors | Models, Biological | Computer Simulation | Biology - methods | Lakes | Environment | Diffusion | Usage | Financial markets | Analysis | Methods | Coral reefs | Ocean circulation | Identification methods | Ecosystems | Extinction | Aquatic ecology | Time series | Biomass | Datasets | Environmental science | Water quality | Water circulation | Quality management | Ocean currents | Biodiversity and Ecology | Environmental Sciences
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Loading RightsIEEE Transactions on Power Systems, ISSN 0885-8950, 05/2010, Volume 25, Issue 2, pp. 667 - 676
This paper proposes a stochastic wind power model based on an autoregressive integrated moving average (ARIMA) process. The model takes into account the...
ARIMA processes | Stochastic processes | Power system planning | time series | Probability distribution | Power system modeling | Power measurement | Wind energy | Wind speed | Wind power generation | Wind farms | Markov processes | Autoregressive processes | Time series | SPEED | FARMS | wind power generation | stochastic processes | ENGINEERING, ELECTRICAL & ELECTRONIC | Usage | Learning models (Stochastic processes) | Autoregression (Statistics) | Distribution (Probability theory) | Design and construction | Wind power | Buildings and facilities | Offshore | Correlation | Driving | Markov models | Stochasticity | Temporal logic
ARIMA processes | Stochastic processes | Power system planning | time series | Probability distribution | Power system modeling | Power measurement | Wind energy | Wind speed | Wind power generation | Wind farms | Markov processes | Autoregressive processes | Time series | SPEED | FARMS | wind power generation | stochastic processes | ENGINEERING, ELECTRICAL & ELECTRONIC | Usage | Learning models (Stochastic processes) | Autoregression (Statistics) | Distribution (Probability theory) | Design and construction | Wind power | Buildings and facilities | Offshore | Correlation | Driving | Markov models | Stochasticity | Temporal logic
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Loading RightsAnnals of Statistics, ISSN 0090-5364, 2018, Volume 46, Issue 6B, pp. 3510 - 3538
A bootstrap procedure for functional time series is proposed which exploits a general vector autoregressive representation of the time series of Fourier...
Bootstrap | Fourier transform | Karhunen–Loève expansion | Principal components | Spectral density operator | VALUED RANDOM-VARIABLES | INEQUALITY | spectral density operator | MULTIVARIATE STOCHASTIC-PROCESSES | STATISTICS & PROBABILITY | principal components | CLT | HILBERT-SPACE | Karhunen Loeve expansion | PREDICTION
Bootstrap | Fourier transform | Karhunen–Loève expansion | Principal components | Spectral density operator | VALUED RANDOM-VARIABLES | INEQUALITY | spectral density operator | MULTIVARIATE STOCHASTIC-PROCESSES | STATISTICS & PROBABILITY | principal components | CLT | HILBERT-SPACE | Karhunen Loeve expansion | PREDICTION
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Loading RightsSensors, ISSN 1424-8220, 08/2019, Volume 19, Issue 16, p. 3611
We present a new seasonal forecasting method based on F1-transform (fuzzy transform of order 1) applied on weather datasets. The objective of this research is...
Statistical analysis | Autoregressive models | Test procedures | Weather forecasting | Artificial neural networks | Time series | Weather stations | Forecasting techniques | Forecasting | Genetic algorithms | Weather data | Fuzzy sets | Climate change | Heat | Mathematical analysis | Neural networks | Data sets | Performance enhancement | Polynomials | Trends | Fuzzy | Methods | seasonal time series | TSSF1 | F-transform | TSSF | F1-transform forecasting
Statistical analysis | Autoregressive models | Test procedures | Weather forecasting | Artificial neural networks | Time series | Weather stations | Forecasting techniques | Forecasting | Genetic algorithms | Weather data | Fuzzy sets | Climate change | Heat | Mathematical analysis | Neural networks | Data sets | Performance enhancement | Polynomials | Trends | Fuzzy | Methods | seasonal time series | TSSF1 | F-transform | TSSF | F1-transform forecasting
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Loading RightsChaos, Solitons and Fractals: the interdisciplinary journal of Nonlinear Science, and Nonequilibrium and Complex Phenomena, ISSN 0960-0779, 12/2017, Volume 105, pp. 38 - 42
Recent works in econophysics have quantitatively shown that the latest global financial crisis has considerably affected nonlinear dynamics in markets...
Shannon entropy | Volatility | Long memory | Kolmogorov complexity | Financial crisis | FRACTALITY | MOVEMENTS | NETWORK | PHYSICS, MULTIDISCIPLINARY | RETURNS | PHYSICS, MATHEMATICAL | CHAOS | MATHEMATICS, INTERDISCIPLINARY APPLICATIONS | AUTOREGRESSIVE CONDITIONAL HETEROSKEDASTICITY | DYNAMICS | STOCK MARKETS | COMMODITY-MARKETS | Foreign securities | Business schools | Financial markets
Shannon entropy | Volatility | Long memory | Kolmogorov complexity | Financial crisis | FRACTALITY | MOVEMENTS | NETWORK | PHYSICS, MULTIDISCIPLINARY | RETURNS | PHYSICS, MATHEMATICAL | CHAOS | MATHEMATICS, INTERDISCIPLINARY APPLICATIONS | AUTOREGRESSIVE CONDITIONAL HETEROSKEDASTICITY | DYNAMICS | STOCK MARKETS | COMMODITY-MARKETS | Foreign securities | Business schools | Financial markets
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Loading Rights1976, Rev. ed., Holden-Day series in time series analysis., ISBN 9780816211043, xxi, 575
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The Annals of Statistics, ISSN 0090-5364, 10/2012, Volume 40, Issue 5, pp. 2601 - 2633
In this paper, we study nonparametric models allowing for locally stationary regressors and a regression function that changes smoothly over time. These models...
Density estimation | Time series models | Approximation | Autoregressive models | Time series | Estimation theory | Stationary processes | Estimators | Point estimators | Perceptron convergence procedure | Smooth backfitting | Local stationarity | Nonparametric regression | nonparametric regression | VARYING ARCH PROCESSES | NONLINEAR AUTOREGRESSIVE MODELS | smooth backfitting | NONSTATIONARY | STATISTICS & PROBABILITY | DEPENDENT DATA | UNIFORM-CONVERGENCE RATES | INFERENCE | GEOMETRIC ERGODICITY | 62M10 | 62G20 | 62G08
Density estimation | Time series models | Approximation | Autoregressive models | Time series | Estimation theory | Stationary processes | Estimators | Point estimators | Perceptron convergence procedure | Smooth backfitting | Local stationarity | Nonparametric regression | nonparametric regression | VARYING ARCH PROCESSES | NONLINEAR AUTOREGRESSIVE MODELS | smooth backfitting | NONSTATIONARY | STATISTICS & PROBABILITY | DEPENDENT DATA | UNIFORM-CONVERGENCE RATES | INFERENCE | GEOMETRIC ERGODICITY | 62M10 | 62G20 | 62G08
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Loading RightsJournal of Applied Econometrics, ISSN 0883-7252, 04/2018, Volume 33, Issue 3, pp. 332 - 354
Summary We propose parametric copulas that capture serial dependence in stationary heteroskedastic time series. We suggest copulas for first‐order Markov...
FREQUENCY DATA | MODELS | EXCHANGE-RATES | CONDITIONAL CORRELATION | SOCIAL SCIENCES, MATHEMATICAL METHODS | LONGITUDINAL DATA | CONSTRUCTIONS | ECONOMICS | TEMPORAL DEPENDENCE | MARKOV-PROCESSES | VOLATILITY | Money | Analysis | Markov processes | Autoregressive models | Correlation analysis | Volatility | Time series
FREQUENCY DATA | MODELS | EXCHANGE-RATES | CONDITIONAL CORRELATION | SOCIAL SCIENCES, MATHEMATICAL METHODS | LONGITUDINAL DATA | CONSTRUCTIONS | ECONOMICS | TEMPORAL DEPENDENCE | MARKOV-PROCESSES | VOLATILITY | Money | Analysis | Markov processes | Autoregressive models | Correlation analysis | Volatility | Time series
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Loading RightsStochastic Processes and their Applications, ISSN 0304-4149, 2009, Volume 119, Issue 4, pp. 1055 - 1080
Extreme values of a stationary, multivariate time series may exhibit dependence across coordinates and over time. The aim of this paper is to offer a new and...
Mixing | Stable random vector | Heavy tails | Vague convergence | Weak convergence | Point processes | Clusters of extremes | Stochastic recurrence equation | Stationary time series | Autoregressive process | Extremal index | Multivariate regular variation | Tail process | Factor GARCH model | BEHAVIOR | ARCH | RENEWAL THEORY | STATISTICS & PROBABILITY | MARKOV-CHAIN | DISTRIBUTIONS | POINT PROCESS | CLUSTERS | CONVERGENCE | FUNCTIONALS | Autoregressive process Clusters of extremes Extremal index Factor GARCH model Heavy tails Mixing Multivariate regular variation Point processes Stable random vector Stationary time series Stochastic recurrence equation Tail process Vague convergence Weak convergence | Analysis | Algorithms
Mixing | Stable random vector | Heavy tails | Vague convergence | Weak convergence | Point processes | Clusters of extremes | Stochastic recurrence equation | Stationary time series | Autoregressive process | Extremal index | Multivariate regular variation | Tail process | Factor GARCH model | BEHAVIOR | ARCH | RENEWAL THEORY | STATISTICS & PROBABILITY | MARKOV-CHAIN | DISTRIBUTIONS | POINT PROCESS | CLUSTERS | CONVERGENCE | FUNCTIONALS | Autoregressive process Clusters of extremes Extremal index Factor GARCH model Heavy tails Mixing Multivariate regular variation Point processes Stable random vector Stationary time series Stochastic recurrence equation Tail process Vague convergence Weak convergence | Analysis | Algorithms
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Loading RightsElectric Power Systems Research, ISSN 0378-7796, 11/2016, Volume 140, pp. 378 - 390
Large-scale utilization of electric vehicles (EVs) affects the total electricity demand considerably. Demand forecast is usually designed for the seasonally...
Charging demand | Autoregressive integrated moving average (ARIMA) | Electric vehicle parking lots | Demand forecasting | Chance-constrained security-constrained unit commitment | PLUG-IN HYBRID | COORDINATION | OPTIMIZATION | ENGINEERING, ELECTRICAL & ELECTRONIC | Electric vehicles | Forecasts and trends | Electric power systems | Analysis
Charging demand | Autoregressive integrated moving average (ARIMA) | Electric vehicle parking lots | Demand forecasting | Chance-constrained security-constrained unit commitment | PLUG-IN HYBRID | COORDINATION | OPTIMIZATION | ENGINEERING, ELECTRICAL & ELECTRONIC | Electric vehicles | Forecasts and trends | Electric power systems | Analysis
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Loading RightsComputational Statistics and Data Analysis, ISSN 0167-9473, 06/2019, Volume 134, pp. 157 - 170
Exact D-optimal Bayesian designs for time series experiments are discussed in this article. This work is motivated by an RNA sequencing experiment and two...
Quasi-likelihood | Autoregressive errors | Poisson | Efficiency | Exact D-optimal designs | Estimating equation | Log-linear | PARAMETER-ESTIMATION | STATISTICS & PROBABILITY | GENERALIZED LINEAR-MODELS | CORRELATED MODEL | COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS | GROWTH CURVE MODELS | POISSON REGRESSION | LIKELIHOOD-ESTIMATION | RNA sequencing | Medicine, Experimental | Medical research | Employee motivation | Analysis
Quasi-likelihood | Autoregressive errors | Poisson | Efficiency | Exact D-optimal designs | Estimating equation | Log-linear | PARAMETER-ESTIMATION | STATISTICS & PROBABILITY | GENERALIZED LINEAR-MODELS | CORRELATED MODEL | COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS | GROWTH CURVE MODELS | POISSON REGRESSION | LIKELIHOOD-ESTIMATION | RNA sequencing | Medicine, Experimental | Medical research | Employee motivation | Analysis
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