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Autoregressive–moving-average model
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41	STAT6087 Time Series Analysis Danny Pfeffermann & Angela Luna-Hernandez University of Southampton Duncan Elliott Add to Reading List Source URL: www.southampton.ac.uk Language: English - Date: 2015-04-13 07:26:41 X-12-ARIMA Time series Forecasting Seasonal adjustment Seasonality Periodogram X12 Exponential smoothing Moving-average model Statistics Time series analysis Autoregressive integrated moving average
42	Nonlin. Processes Geophys., 21, 1159–1168, 2014 www.nonlin-processes-geophys.net[removed]doi:[removed]npg[removed] © Author(s[removed]CC Attribution 3.0 License. An improved ARIMA model for precipitation simu Add to Reading List Source URL: www.nonlin-processes-geophys.net Language: English - Date: 2014-12-04 10:35:01 Box–Jenkins Seasonality Time series Moving-average model Partial autocorrelation function Forecasting Regression analysis Climate Hydrology Statistics Time series analysis Autoregressive integrated moving average
43	Beyond ARMA Models: Nonstationarity & Seasonality • now know how to handle time series in a certain ‘comfort zone’ − no worrisome trends or seasonal patterns − sample ACF and PACF decay fairly rapidly − simpl Add to Reading List Source URL: faculty.washington.edu Language: English - Date: 2015-02-27 11:14:32 XT Partial autocorrelation function Moving-average model Statistics Autoregressive integrated moving average Noise
44	Global Journal of Research Business VOLUME 7 Add to Reading List Source URL: www.theibfr.com Language: English - Date: 2013-09-06 02:54:06 Autoregressive integrated moving average Forecasting Autoregressive conditional heteroskedasticity Economic model Box–Jenkins Macroeconomic model Time series Linear regression Regression analysis Statistics Econometrics Time series analysis
45	Electric power load forecasting using periodic piece-wise linear models This is an English translation. The original version is in Swedish. Examine thesis in control theory, Add to Reading List Source URL: hem.fyristorg.com Language: English - Date: 2002-08-28 07:09:14 Noise Estimation theory Regression analysis Forecasting Linear regression Autoregressive model Autoregressive conditional heteroskedasticity Autoregressive–moving-average model Linear prediction Statistics Econometrics Time series analysis
46	A Pattern Test for Distinguishing Between Autoregressive and Mean-Shift Data WAYNE A. TAYLOR Baxter Healthcare Corporation, Round Lake, IL[removed]Statistical methods such as control charts and change-point analysis are co Add to Reading List Source URL: www.variation.com Language: English - Date: 2014-12-31 23:27:20 Noise Autoregressive model Normal distribution Autocorrelation Covariance Durbin–Watson statistic Autoregressive–moving-average model Statistics Time series analysis Covariance and correlation
47	Visual Analytics Methods to Guide Diagnostics for Time Series Model Predictions ¨ W. Aigner, P. Filzmoser, T. Gschwandtner, T. Lammarsch, S. Miksch, and A. Rind M. Bogl, (a) (d) Add to Reading List Source URL: predictive-workshop.github.io Language: English - Date: 2014-10-21 11:34:48 Box–Jenkins Forecasting Autoregressive integrated moving average Prediction Time series Economic model Predictive Model Markup Language Protein structure prediction Statistics Time series analysis Predictive analytics
48	Rob J Hyndman Forecasting: Principles and Practice 7. Non-seasonal ARIMA models Add to Reading List Source URL: robjhyndman.com Language: English - Date: 2014-09-16 02:20:25 Forecasting Autoregressive integrated moving average Moving-average model Autoregressive model Economic model Statistics Time series analysis Noise
49	Forecasting time series using R 1 Forecasting time series using R Add to Reading List Source URL: robjhyndman.com Language: English - Date: 2011-10-27 00:53:06 Forecasting Statistical forecasting Time series Autoregressive integrated moving average Macroeconomic model Exponential smoothing Lee-Carter model Statistics Time series analysis Data analysis
50	Classical Decomposition Model Revisited • recall classical decomposition model Xt = mt + st + Wt (∗) Add to Reading List Source URL: faculty.washington.edu Language: English - Date: 2015-03-09 15:00:30 Structure Mathematics Mathematical sciences Noise Autoregressive integrated moving average Moving-average model

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