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Autoregressive model
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61	Are Stocks Really Less Volatile in the Long Run? ˇ ´ LUBO Sˇ PASTOR Add to Reading List Source URL: www.q-group.org Language: English - Date: 2016-03-25 20:51:45 Statistics Data analysis Analysis Mathematical finance Least squares Variance Standard deviation Volatility Coefficient of determination Autoregressive model Covariance and contravariance Expected value
62	Visual Analytics for Development and Evaluation of Order Selection Criteria for Autoregressive Processes ¨ ¨ Thomas Lowe Student Member, IEEE, Emmy-Charlotte Forster Add to Reading List Source URL: www.cg.cs.tu-bs.de Language: English - Date: 2015-10-22 08:45:41 Model selection Time series analysis Signal processing Statistical models Noise Akaike information criterion HannanQuinn information criterion Bayesian information criterion Autoregressive model Predictive analytics Unit root Autocorrelation
63	Is the reduction in output growth related to the increase in its uncertainty? The case of Italy MENELAOS KARANASOS Busines School Brunel University Add to Reading List Source URL: www.mkaranasos.com Language: English - Date: 2015-05-18 13:58:20 Mathematical finance Autoregressive conditional heteroskedasticity Econometrics Time series analysis Economic model Economic growth Volatility Real business-cycle theory Time series Causality Macroeconomics Standard deviation
64	Homework 4 solutions Joe Neeman October 27, We began by looking at the ACF of the original data sequence (Figure 1), which seems to decay very slowly. In particular, the process is probably not an ARMA process. T Add to Reading List Source URL: www.stat.berkeley.edu Language: English - Date: 2010-11-23 19:26:03 Time series models Noise Time series analysis Covariance and correlation Moving-average model Partial autocorrelation function Autoregressive integrated moving average Akaike information criterion QQ plot BoxJenkins
65	Introduction to Time Series Analysis. Lecture 6. Peter Bartlett www.stat.berkeley.edu/∼bartlett/courses/153-fall2010 Last lecture: 1. Causality Add to Reading List Source URL: www.stat.berkeley.edu Language: English - Date: 2010-09-14 17:35:35 Noise Time series models Time series analysis Autoregressivemoving-average model Autoregressive model Moving-average model
66	Introduction to Time Series Analysis. LectureReview: Spectral density 2. Examples 3. Spectral distribution function. 4. Autocovariance generating function and spectral density. Add to Reading List Source URL: www.stat.berkeley.edu Language: English - Date: 2010-10-28 01:41:25 Signal processing Statistics Electrical engineering Mathematical analysis Autocorrelation Spectral density Autoregressive model
67	Modeling sequential data using higher-order relational features and predictive training Vincent Michalski Goethe-University Frankfurt, Frankfurt, Germany Add to Reading List Source URL: arxiv.org Language: English - Date: 2014-02-11 21:17:06 Applied mathematics Artificial neural networks Machine learning Statistics Probability and statistics Computational neuroscience Regression analysis Computational statistics Deep learning Autoregressive model Recurrent neural network Overfitting
68	Estimation in High-dimensional Vector Autoregressive Models with Noisy Data Kam Chung Wong1 and Ambuj Tewari2 1 Department of Statistics, University of Michigan, Ann Arbor Add to Reading List Source URL: ctools.umich.edu Language: English Time series models Signal processing Econometrics Noise Vector autoregression Autoregressive model Value at risk Time series Vector
69	Improving the Forecast of Freight Transport Demand Using Machine Learning and Time Series Methods Dr. Clare Jackson, Institute for Transport Planning and Systems, Swiss Federal Institute of Technology (ETHZ) Dr. Dirk Bru Add to Reading List Source URL: www.strc.ch Language: English - Date: 2014-05-06 08:57:24 Prediction Statistical forecasting Time Future Forecasting Demand forecasting Transportation forecasting Artificial neural network Autoregressive model Time series Solar power forecasting Wind power forecasting
70	Robust Multivariate Autoregression for Anomaly Detection in Dynamic Product Ratings Nikou Günnemann Stephan Günnemann Add to Reading List Source URL: www.cs.cmu.edu Language: English - Date: 2014-01-26 13:10:01 Statistics Probability distributions Time series models Robust statistics Statistical theory Regression analysis Outlier Autoregressive model Errors and residuals Normal distribution Principal component analysis Exponential distribution

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