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  1. Periodic Time Series Models
  2. RePub, Erasmus University Repository: Periodic Time Series Models
  3. partsm: Periodic Autoregressive Time Series Models

In the literature, this estimator has been widely used to deal with large data sets, since, in this context, its performance is similar to the Gaussian maximum likelihood estimator and the estimates are obtained much faster.

Periodic Time Series Models

Here, the usefulness of Whittle estimator is illustrated by a Monte Carlo simulation and by fitting the periodic autoregressive moving average model to daily mean concentrations of particulate matter observed in Cariacica, Brazil. The results confirm the potentiality of Whittle estimator when applied to periodic time series.

Article :. DOI: In the second model, the autoregression coefficients consist of the additive periodic effects of several nominal variables for example, the effect of hour in a given day and the effect of day in a given week for hourly data. Truncated Fourier representations of different periods are used to parametrize the autoregression coefficients in the two models.

Time Series Forecasting Theory - AR, MA, ARMA, ARIMA - Data Science

Model estimation and inference through ordinary and weighted least squares, and model selection based on diagnostics plots, in particular, are considered for the two approaches. An application to a real time series of hourly electricity volumes from the Nord Pool Spot Exchange is also presented, where the nature and use of the two models are contrasted. Please log in to get access to this content Log in Register for free. To get access to this content you need the following product:.


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RePub, Erasmus University Repository: Periodic Time Series Models

Prentice Hall Inc. Dannecker L Energy time series forecasting: efficient and accurate forecasting of evolving time series from the energy domain. Springer, Heidelberg CrossRef.

In: Cyclostationarity: theory and methods-II. Franses Philip Hans and R. Paap Richard. This book considers periodic time series models for seasonal data, characterized by parameters that differ across the seasons, and focuses on their usefulness for out-of-sample forecasting.

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partsm: Periodic Autoregressive Time Series Models

Providing an up-to-date survey of the recent developments in periodic time series, the book presents a large number of empirical results. The first part of the book deals with model selection, diagnostic checking and forecasting of univariate periodic autoregressive models. Tests for periodic integration, are discussed, and an extensive discussion of the role of deterministic regressors in testing for periodic integration and in forecasting is provided.