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1. Full Text Threshold models in time series analysis—Some reflections
by Tong, Howell
Journal of econometrics, ISSN 0304-4076, 12/2015, Volume 189, Issue 2, pp. 485 - 491
Panel threshold model | Mixture of distributions | Splines | Volatility | Threshold unit root | Positive-valued time series | Structural breaks | Threshold autoregressive models | Nonlinear unit root | Non-stationarity | Catastrophe | Stochastic volatility | GARCH model | Markov switching model | Wrong model | Hysteresis | Mis-specified model | Non-likelihood approach | Threshold moving average models | Threshold principle | All-step-ahead prediction | Hidden Markov chain | Smooth threshold autoregressive models | Asymmetry | Bayesian decision | Business cycle | Conditionally heteroscedastic autoregressive models with thresholds | Open-loop system | Jump resonance | Economics | Mathematics, Interdisciplinary Applications | Physical Sciences | Social Sciences | Social Sciences, Mathematical Methods | Business & Economics | Mathematics | Mathematical Methods In Social Sciences | Science & Technology | Studies | Econometrics | Time series
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2. Full Text LASSO GUARANTEES FOR beta-MIXING HEAVY-TAILED TIME SERIES
by Wong, Kam Chung and Li, Zifan and Tewari, Ambuj
The Annals of statistics, ISSN 0090-5364, 04/2020, Volume 48, Issue 2, pp. 1124 - 1142
Statistics & Probability | Physical Sciences | Mathematics | Science & Technology | Errors | Gaussian process | Autoregressive models | Normal distribution | Time series | Linear transformations | Random variables
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3. Full Text NETS: Network estimation for time series
by Barigozzi, Matteo and Brownlees, Christian
Journal of applied econometrics (Chichester, England), ISSN 0883-7252, 04/2019, Volume 34, Issue 3, pp. 347 - 364
Economics | Business & Economics | Social Sciences | Mathematical Methods In Social Sciences | Social Sciences, Mathematical Methods | Economic models | Matrix algebra | Volatility | Variability | Time series | Innovations | Graph theory | Covariance matrix | Regression analysis | Matrix methods | Forecasting | Algorithms | Covariance | Mathematical analysis | Matrices | Graphical representations
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4. Full Text Poisson QMLE of Count Time Series Models
by Ahmad, Ali and Francq, Christian
Journal of time series analysis, ISSN 0143-9782, 05/2016, Volume 37, Issue 3, pp. 291 - 314
integer‐valued AR and GARCH models | time series of counts | non‐normal asymptotic distribution | Poisson quasi‐maximum likelihood estimator | consistency and asymptotic normality | Boundary of the parameter space | Time series of counts | Consistency and asymptotic normality | Poisson quasi-maximum likelihood estimator | Integer-valued AR and GARCH models | Non-normal asymptotic distribution | Mathematics, Interdisciplinary Applications | Statistics & Probability | Physical Sciences | Mathematics | Science & Technology | Monte Carlo method | Analysis | Models | Studies | Statistical analysis | Poisson distribution | Time series | Economics and Finance | Humanities and Social Sciences
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5. Full Text Autoregressive models for matrix-valued time series
by Chen, Rong and Xiao, Han and Yang, Dan
Journal of econometrics, ISSN 0304-4076, 08/2020
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6. Full Text Innovative hybrid models for forecasting time series applied in wind generation based on the combination of time series models with artificial neural networks
by Camelo, Henrique do Nascimento and Lucio, Paulo Sérgio and Leal Junior, João Bosco Verçosa and Carvalho, Paulo Cesar Marques de and Santos, Daniel von Glehn dos
Energy (Oxford), ISSN 0360-5442, 05/2018, Volume 151, pp. 347 - 357
Wind generation | Exogenous variables | Predictability | Time series | Artificial neural networks | Thermodynamics | Physical Sciences | Technology | Energy & Fuels | Science & Technology | Neural networks | Models | Precipitation (Meteorology) | Statistical analysis | Error analysis | Autoregressive models | Model accuracy | Seasonal variations | Wind speed | Mathematical analysis | Research methodology | Predictions | Mathematical models
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7. Full Text Analysis of ecological time series with ARMA( p,q ) models
by Anthony R. Ives and Karen C. Abbott and Nicolas L. Ziebarth
Ecology (Durham), ISSN 0012-9658, 3/2010, Volume 91, Issue 3, pp. 858 - 871
Datasets | Simulations | Population ecology | Maximum likelihood estimation | Ecological modeling | Time series models | Estimation bias | Time series | Autoregressive moving average | Modeling | autoregressive moving average (ARMA) models | model selection | restricted maximum likelihood (REML) parameter estimation | return time | stability | estimation bias | Autoregressive moving average (ARMA) models | Restricted maximum likelihood (REML) parameter estimation | Stability | Model selection | Return time | Environmental Sciences & Ecology | Ecology | Life Sciences & Biomedicine | Science & Technology | Fundamental and applied biological sciences. Psychology | Biological and medical sciences | General aspects | Animal and plant ecology | Animal, plant and microbial ecology | Animals | Time Factors | Galliformes - physiology | Models, Biological | Computer Simulation | Ecosystem | Models, Statistical | Population Dynamics | Autoregression (Statistics) | Models | Ecological research | Methods | Parameter estimation | Stochastic models | Maximum likelihood method | Index Medicus
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8. Full Text Sieve bootstrap for functional time series
by Paparoditis, Efstathios
The Annals of statistics, ISSN 0090-5364, 2018, Volume 46, Issue 6B, pp. 3510 - 3538
Bootstrap | Fourier transform | Karhunen–Loève expansion | Principal components | Spectral density operator | Statistics & Probability | Physical Sciences | Mathematics | Science & Technology | Fourier transforms | Statistical analysis | Functional testing | Dependence | Time series | Fourier analysis | Bootstrap method | Statistical methods | Fourier series | Data assimilation | Autoregressive processes
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9. Full Text COPAR—multivariate time series modeling using the copula autoregressive model
by Brechmann, Eike Christian and Czado, Claudia
Applied stochastic models in business and industry, ISSN 1524-1904, 07/2015, Volume 31, Issue 4, pp. 495 - 514
vector autoregression | forecasting time series | copula autoregression | vine copula | multivariate time series | Economics | Time series | Inference | Nonlinearity | Mathematical models | Raw materials | Business | Autoregressive processes
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10. Full Text A new hybrid method for time series forecasting: AR–ANFIS
by Sarıca, Busenur and Sarıca, Busenur and Eğrioğlu, Erol and Eğrioğlu, Erol and Aşıkgil, Barış and Aşıkgil, Barış
Neural computing & applications, ISSN 0941-0643, 2/2018, Volume 29, Issue 3, pp. 749 - 760
Data Mining and Knowledge Discovery | Time series | Autoregressive model | Computational Science and Engineering | Particle swarm optimization | Computational Biology/Bioinformatics | Fuzzy inference system | Adaptive network fuzzy inference system | Computer Science | Image Processing and Computer Vision | Artificial Intelligence (incl. Robotics) | Fuzzy C-Means | Probability and Statistics in Computer Science | Computer Science, Artificial Intelligence | Technology | Science & Technology | Models | Neural networks | Mathematical optimization | Analysis | Methods | Adaptive systems | Artificial neural networks | Swarm intelligence | Mathematical models | Forecasting | Augmented reality | Autoregressive processes | Fuzzy systems
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11. Full Text NONPARAMETRIC REGRESSION FOR LOCALLY STATIONARY TIME SERIES
by Michael Vogt
The Annals of statistics, ISSN 0090-5364, 10/2012, Volume 40, Issue 5, pp. 2601 - 2633
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 | Statistics & Probability | Physical Sciences | Mathematics | Science & Technology | Studies | Mathematical models | Regression analysis | Theoretical mathematics | 62M10 | nonparametric regression | 62G20 | 62G08 | smooth backfitting
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12. Full Text Seasonal Time Series Forecasting by F1-Fuzzy Transform
by Di Martino and Sessa
Sensors (Basel, Switzerland), ISSN 1424-8220, 08/2019, Volume 19, Issue 16, p. 3611
Statistical analysis | Autoregressive models | Test procedures | Weather forecasting | Artificial neural networks | Time series | Weather stations | Forecasting techniques | Forecasting | Genetic algorithms | Fuzzy sets | Climate change | Heat | Meteorological data | Mathematical analysis | Neural networks | Data sets | Performance enhancement | Polynomials | Trends | Methods | Index Medicus | seasonal time series | TSSF1 | F-transform | TSSF | F1-transform forecasting
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13. Full Text Modelling heavy-tailedness in count time series
by Qian, Lianyong and Li, Qi and Zhu, Fukang
Applied mathematical modelling, ISSN 0307-904X, 06/2020, Volume 82, pp. 766 - 784
H-step forecast | Maximum likelihood | Diagnostics | Heavy-tailedness | Generalized Poisson-inverse Gaussian | Count time series | Mathematics, Interdisciplinary Applications | Engineering | Physical Sciences | Technology | Engineering, Multidisciplinary | Mechanics | Mathematics | Science & Technology | Parameter estimation | Gaussian process | Statistical analysis | Autoregressive models | Normal distribution | Gaussian distribution | Electric utilities | Modelling | Normality | Poisson distributions | Autoregressive processes
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