Journal ArticleDOI
An Algorithm for Exact Maximum Likelihood Estimation of Autoregressive-Moving Average Models by Means of Kalman Filtering
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This article is published in Applied statistics.The article was published on 1980-11-01. It has received 175 citations till now. The article focuses on the topics: Expectation–maximization algorithm & Maximum likelihood sequence estimation.read more
Citations
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Journal ArticleDOI
Forecasting time series with complex seasonal patterns using exponential smoothing
TL;DR: In this article, an innovations state space modeling framework is introduced for forecasting complex seasonal time series such as those with multiple seasonal periods, high-frequency seasonality, non-integer seasonality and dual-calendar effects.
Journal ArticleDOI
Modeling persistence in hydrological time series using fractional differencing
TL;DR: In this paper, the authors generalized the autoregressive integrated moving average (ARIMA) time series models by permitting the degree of differencing d to take fractional values.
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Short-Term Traffic Flow Forecasting: An Experimental Comparison of Time-Series Analysis and Supervised Learning
TL;DR: The SARIMA model coupled with a Kalman filter is the most accurate model; however, the proposed seasonal support vector regressor turns out to be highly competitive when performing forecasts during the most congested periods.
Journal ArticleDOI
Trades, quotes, inventories, and information
TL;DR: In this paper, an empirical examination of the relation between trades and quote revisions for New York Stock Exchange-listed stocks is designed to ascertain asymmetric-information and inventory-control effects.
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Estimating Missing Observations in Economic Time Series
Andrew Harvey,Richard Pierse +1 more
TL;DR: In this paper, the maximum likelihood estimation of the parameters in an ARIMA model when some of the observations are missing or subject to temporal aggregation is considered, and both problems can be solved by setting up the model in state space form and applying the Kalman filter.
References
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Book
Time series analysis, forecasting and control
TL;DR: In this article, a complete revision of a classic, seminal, and authoritative book that has been the model for most books on the topic written since 1970 is presented, focusing on practical techniques throughout, rather than a rigorous mathematical treatment of the subject.
Journal ArticleDOI
Time Series Analysis Forecasting and Control
TL;DR: This revision of a classic, seminal, and authoritative book explores the building of stochastic models for time series and their use in important areas of application forecasting, model specification, estimation, and checking, transfer function modeling of dynamic relationships, modeling the effects of intervention events, and process control.
Journal ArticleDOI
Maximum likelihood estimation of regression models with autoregressive-moving average disturbances
Andrew Harvey,G. D. A. Phillips +1 more
TL;DR: In this paper, the regression model with autoregressive-moving average disturbances is cast in a form suitable for the application of Kalman filtering techniques, which enables the generalized least squares estimator to be calculated without evaluating and inverting the covariance matrix of the disturbances.