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Topic: Autoregressive integrated moving average

	Autoregressive moving average model - Wikipedia, the free encyclopedia
		An autoregressive model is essentially an infinite impulse response filter with some additional interpretation placed on it.
		The moving average model is essentially a finite impulse response filter with some additional interpretation placed on it.
		Autoregressive moving average models can be generalized in other ways.
	en.wikipedia.org /wiki/Autoregressive_moving_average_model (846 words)

	AUTOREGRESSIVE INTEGRATED MOVING AVERAGE MODELS (ARIMA)
		Moving average parameters relate what happens in period t only to the random errors that occurred in past time periods, i.e.
		A moving average model with one MA term may be written as follows...
		As in the case of autoregressive models, the moving average models can be extended to higher order structures covering different combinations and moving average lengths.
	www.forecastingsolutions.com /arima.html (1127 words)

	Springer Online Reference Works
		The class of Gaussian autoregressive moving-average processes coincides with the class of stationary processes that have a spectral density and are one-dimensional components of multi-dimensional Markov processes (see [3]).
		Generalizations of autoregressive moving-average processes are the autoregressive integrated moving-average processes introduced by G.E.P. Box and G.M. Jenkins (see [1]) and often used in applied problems.
		Autoregressive moving-average processes are used by statisticians [a3], econometricians [a1] and engineers [a5].
	eom.springer.de /m/m064200.htm (286 words)

	Applying Short-term Traffic Prediction Models for Updating
		Average errors for ARIMA model are usually between 10% and 15%.
		For C002181t, average errors for regression models are less than 1%, and those for neural network models are between 3% and 7%, as shown in Table 4.
		Average errors for factor models are usually between 4% to 7%.
	www.autobox.com /traffic.html (7332 words)

	[No title]
		Referring to the full syntax, depvar is the variable being modeled and the structural or regression part of the model is specified in varlist.
		The options ^ar()^ and ^ma()^ are used to specify the lags of autoregressive and moving-average terms respectively.
		^ma(^numlist^)^ specifies the moving average terms to be included in the model.
	www.stata.com /updates/ado/arima.hlp (1331 words)

	On non stationarity and antipersistency in global temperature series (Site not responding. Last check: 2007-09-06)
		The family of autoregressive integrated moving average (ARIMA) models is used to represent the monthly anomalies with a remarkably successful outcome in terms of residual variance.
		Moving to small M value we will get into a region where the estimates of H are very scattered and unreliable because there are not enough frequencies left to have an accurate regression.
		The averaging adds one necessary MA term to initial model (because of the averaging grid is independent of the temperature), and (0, 1, 1) model is fitted.
	mitosyfraudes.8k.com /Calen/karner.html (7703 words)

	Traders' Glossary A-C
		The index is often smoothed with a simple moving average.
		Takes the average rate of return for the last 36 months and divides it by the maximum drawdown for the same period.
		As applied to two portfolios, a high correlation coefficient for the relative returns indicates that the portfolio values have moved in tandem and a low correlation coefficient means the opposite.
	www.traders.com /Documentation/RESource_docs/Glossary/glossary.html (2848 words)

	Time Series Analysis and Forecasting
		The goal of this book is to distill and integrate these research results into cohesive and comprehensible methodologies, and to provide a streamlined approach to time series analysis and forecasting.
		Various ad hoc methods, including those using moving averages and weighted smoothing, had been in use long before model-based forecasting methods were widely accepted.
		Averaging methods are developed based on an average of weighted observations.
	www.scausa.com /tsfbook.htm (4245 words)

	BioMed Central \| Full text \| Time series modeling for syndromic surveillance
		The yearly average profile of the resulting signal was calculated, yielding a model of the average utilization patterns for each day of the year.
		A separate yearly ensemble average profile of respiratory-related visits (Figure 4) showed a general increase in respiratory-related visits during the winter, together with localized peaks in the fall and spring.
		This means that that on average, the forecasts of the model were to within an average of 13 visits of the actual daily volume, which averaged 137 visits per day.
	www.biomedcentral.com /1472-6947/3/2 (3564 words)

	Optionetics - Glossary
		The purpose of this quantifying is to determine the ease, or lack thereof, with which a particular issue is able to move in one direction or another.
		A moving average is then applied to the EMV value-the moving average period can be varied in order to make the EMV flexible as a trading tool.
		A bond's average price is calculated by adding its face value to the price paid for it and dividing the result by two.
	www.optionetics.com /education/glossary.asp (3758 words)

	Article: ARIMA Model (Site not responding. Last check: 2007-09-06)
		ARIMA (AutoRegressive Integrated Moving Average) model, introduced by Box and Jenkins in 1976, includes three types of parameters: the autoregressive parameters (p), the number of differencing passes (d), and moving average parameters (q).
		One autoregressive (p) and one moving average (q) parameter.
		For example, the model (0,1,2)(0,1,1) describes a model that includes no autoregressive parameters, 2 regular moving average parameters and 1 seasonal moving average parameter, and these parameters were computed for the series after it was differenced once with lag 1, and once seasonally differenced.
	www.public.iastate.edu /~qmeng/articles/arima.html (700 words)

	Glossary (Part 1)
		ARIMA (AutoRegressive Integrated Moving Average) models are often used to forecast time series data.
		It then calculates the average rank of the values within each group and computes a statistic to determine whether there are significant differences between those average ranks.
		The average of the most recent K data values, where K is the "order" or "span" of the moving average.
	espse.ed.psu.edu /edpsych/faculty/rhale/statistics/statlets/usermanual/glossary.htm (6132 words)

	ARIMA Modeling
		Autoregressive integrated moving-average (ARIMA) models are useful for a wide variety of time series analyses, including forecasting, quality control, seasonal adjustment, and spectral estimation, as well as providing summaries of the data.
		Enter the vector of initial values for the AR coefficients to be used by the optimizer.
		Enter the vector of initial values for the MA coefficients to be used by the optimizer.
	fas.sfu.ca /doc/help/guihelp/__hhelp/arima_modeling.htm (504 words)

	Autoregressive integrated moving average - Wikipedia, the free encyclopedia
		In statistics, an autoregressive integrated moving average (ARIMA) model is a generalisation of an autoregressive moving average or (ARMA) model.
		These models are fitted to time series data either to better understand the data or to predict future points in the series.
		The model is generally referred to as an ARIMA(p,d,q) model where p, d, and q are integers greater than or equal to zero and refer to the order of the autoregressive, integrated, and moving average parts of the model respectively.
	en.wikipedia.org /wiki/Autoregressive_integrated_moving_average (413 words)

	Methods of Computational Intelligence
		The "Autoregressive Moving Average" ARMA models combine autoregressive and moving average terms with a term for past errors.
		The synonym ARIMA is composed of the terms "Moving Average" (MA), "Autoregressive" (AR) and "Integrated" (I) that stands for the differing of the time series (Hansmann 1983; Stock; and Watson 1999).
		The Moving Average (MA) uses instead of the explanatory variables of the lag structure the error terms of the past whereby a dependence between the successive error terms is implied (Fildes, Hibon; et al.
	www.swantje-pietsch.de /arima.html (568 words)

	BJEST
		BJEST estimates the parameters of an ARIMA (AutoRegressive Integrated Moving Average) univariate time series model by the method of conditional or exact maximum likelihood.
		If the process is a pure moving average process, this backcast becomes zero after a fixed number of time periods; consequently, you can set NBACK to a fairly small number in this case.
		NMA= the number of moving average parameters to be estimated.
	www.tspintl.com /products/tsphelp/bjest.htm (1831 words)

	Forecast Pro \| Resources
		The average level of a time series in the general neighborhood of a given point in time.
		The average rate of increase of a time series in the general neighborhood of a given point in time.
		It is calculated as the average of the unsigned percentage errors.
	www.forecastpro.com /resources/glossary/index.html (948 words)

	General Notation for ARIMA Models
		ARIMA is an acronym for AutoRegressive Integrated Moving-Average.
		Thus, when an autoregressive operator and a mean term are both included in the model, the constant term for the model can be represented as
		ARIMA models for time series with regular seasonal fluctuations often use differencing operators and autoregressive and moving average parameters at lags that are multiples of the length of the seasonal cycle.
	www.asu.edu /sas/sasdoc/sashtml/ets/chap7/sect8.htm (605 words)

	Tinbergen Institute - Discussion papers
		Although the main interest in the modelling of electricity prices is often on volatility aspects, we argue that stochastic heteroskedastic behaviour in prices can only be modelled correctly when the conditional mean of the time series is properly modelled.
		In this paper we consider different periodic extensions of regression models with autoregressive fractionally integrated moving average disturbances for the analysis of daily spot prices of electricity.
		For daily prices at three emerging electricity markets that we consider (APX in The Netherlands, EEX in Germany and Powernext in France) periodicity in the autoregressive coefficients is also established, but evidence of long memory is not found and existence of dynamic behaviour in the variance of the spot prices is less pronounced.
	www.tinbergen.nl /scripts/papers.pl?paper=03071.rdf (296 words)

	Mortality seasonality in Escazu, Costa Rica, 1851-1921 Human Biology - Find Articles (Site not responding. Last check: 2007-09-06)
		where Y sub t is the time series, a, is the white noise, (1 - B) is the backward shift operator, (1 - B sup 12) is the seasonal backward shift operator, 0.17 is the autoregressive parameter, 0.88 is the moving average parameter, and 0.88 is the seasonal moving average parameter.
		Figure 5 shows the ACF of the 1892-1921 series.(Figure 5 omitted) It indicates that an AR model is appropriate, because the ACF values decline exponentially after lag 1.
		where Y sub t is the time series, a, is the white noise, 0.43 is the first autoregressive parameter, and O. 11 is the second autoregressive parameter.
	www.findarticles.com /p/articles/mi_qa3659/is_199406/ai_n8725138/pg_6 (624 words)

	ARIMA
		Forecasting and estimating the future: AutoRegressive Integrated Moving Average.
		But modern statistical methods, econometric models, and Business Intelligence software can to some extent help businesses to forecast and to estimate what is going to happen in the future.
		ARIMA model identification uses autoregressions to identify the underling model.
	www.12manage.com /methods_arima.html (240 words)

	Application of ANN for Reservoir Inflow Prediction and Operation (Site not responding. Last check: 2007-09-06)
		The project has primarily two objectives: to provide irrigation to 128,000 ha of agricultural land and to generate 600 MW of electric power.
		An autoregressive integrated moving average time-series model and an ANN-based model were fitted to the monthly inflow data series and their performances were compared.
		The ANN was found to model the high flows better, whereas low flows were better predicted through the autoregressive integrated moving average better, whereas low flows were better predicted through the autoregressive integrated moving average model.
	www.pubs.asce.org /WWWdisplay.cgi?9903868 (232 words)

	INFO 655 - Forecasting Methods and Applications (Site not responding. Last check: 2007-09-06)
		The course is about statistical models that can be used to produce forecasts of important process variables.
		Three quantitative approaches to forecasting are emphasized: Regression methods, Exponential smoothing methods, and Autoregressive integrated moving average (ARIMA) models.
		This project will involve collecting data and documenting the source(s), developing and justifying a forecasting procedure, generating forecasts and error limits (if appropriate), and a summary discussion of the implications of the results.
	wehner.tamu.edu /finc.www/PhD/main/info655.htm (179 words)

	Time series - Wikipedia, the free encyclopedia
		Three broad classes of practical importance are the autoregressive (AR) models, the integrated (I) models, and the moving average (MA) models.
		These three classes depend linearly on previous data points and are treated in more detail in the articles autoregressive moving average models (ARMA) and autoregressive integrated moving average (ARIMA).
		Non-linear dependence on previous data points is of interest because of the possibility of producing a chaotic time series.
	en.wikipedia.org /wiki/Time_series (404 words)

	Influenza and the Winter Increase in Mortality in the United States, 1959-1999 -- Reichert et al. 160 (5): 492 -- ...
		We characterized the underlying trend using the moving average
		The Serfling model baseline was the overall best fit to the data from all nonwinter months, and it assumed a sinusoidal shape.
		The z-like score (ZLS) baseline was computed as the Hamming window-weighted average of the mortality for the 13-month period centered on each monthly data point.
	aje.oxfordjournals.org /cgi/content/full/160/5/492 (5176 words)

	Inference and Forecasting for Fractional Autoregressive Integrated Moving Average Models, with an application to US and ... (Site not responding. Last check: 2007-09-06)
		Inference and Forecasting for Fractional Autoregressive Integrated Moving Average Models, with an application to US and UK inflation (ResearchIndex)
		Inference and Forecasting for Fractional Autoregressive Integrated Moving Average Models, with an application to US and UK inflation (1999)
		3 Fractional integration with drift: Estimation in small sampl..
	citeseer.ist.psu.edu /268035.html (901 words)

	Fall 2004 Graduate Courses
		What is the difference between a fortune teller with a crystal ball and a forecaster with knowledge of time series techniques?
		Find out by learning the basic theory and application of regression, exponential smoothing, and the autoregressive integrated moving average (ARIMA) modeling and forecasting of univariate time series.
		Frequency-domain techniques will also be discussed, including the estimation of spectral density functions and performing tests of white noise and hidden periodicities.
	www.gwu.edu /~stat/Spring06_courses.htm (1010 words)

	Statistical methodology: V. Time series analysis using autoregressive integrated moving average (ARIMA) models -- ...
		Statistical methodology: V. Time series analysis using autoregressive integrated moving average (ARIMA) models
		series methodologies such as autoregressive integrated moving average
		This method is demonstrated by an example from a public
	www.aemj.org /cgi/content/abstract/5/7/739 (169 words)

	Inference and forecasting for fractional autoregressive integrated moving average models; with an application to US and ...
		Inference and forecasting for fractional autoregressive integrated moving average models; with an application to US and UK inflation
		We investigate the relevance of the differences between the methods for the time-series analysis of monthly core consumer price inflation in the US and quarterly overall consumer price inflation in the UK.
		We concentrate on (stationarity) tests for the order of integration and on inference for out-of-sample forecasts of the price level.
	ideas.repec.org /p/dgr/eureir/1999171.html (616 words)

	Unit Roots in Macroeconomic Data (Site not responding. Last check: 2007-09-06)
		Tests for unit roots in autoregressive models (tests for stationarity) are popular in the macroeconomics literature.
		Monte Carlo experiments in Schwert [1987] show that unit root tests derived for pure autoregressive processes have different sampling distributions when the true process is a mixed autoregressive-integrated moving average (ARIMA) process.
		Tests suggested by Said and Dickey [1984,1985], Phillips [1987], Phillips and Perron [1986] and Dickey and Fuller [1979,1981] are applied to a variety of monthly and quarterly macroeconomic time series to illustrate the effects of ARIMA model specification on inferences about stationarity.
	schwert.ssb.rochester.edu /jmeroot.htm (187 words)

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