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Journal of Statistical Software (2008), 27(3)

Rob J. Hyndman and Yeasmin Khandakar

Abstract: Automatic forecasts of large numbers of univariate time series are often needed in business and other contexts. We describe two automatic forecasting algorithms that have been implemented in the forecast package for R. The first is based on innovation state space models that underly exponential smoothing methods. The second is based on ARIMA models. The algorithms are applicable to both seasonal and non-seasonal data, and are compared and illustrated using four real time series. We also briefly describe some of the other functionality available in the forecast package.

Keywords: ARIMA models, automatic forecasting, exponential smoothing, prediction intervals, state space models, time series, R.

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March 28th, 2017

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Coherent Probabilistic Forecasts for Hierarchical Time Series

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stR package for R

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thief package for R

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ISCRR time series workshop

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On sampling methods for costly multi-objective black-box optimization

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Dynamic Algorithm Selection for Pareto Optimal Set Approximation

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Forecasting uncertainty in electricity smart meter data by boosting additive quantile regression

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Bayesian rank selection in multivariate regressions

January 25th, 2016

Probabilistic Energy Forecasting: Global Energy Forecasting Competition 2014 and Beyond

January 24th, 2016

Long-term forecasts of age-specific participation rates with functional data models

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Bagging exponential smoothing methods using STL decomposition and Box-Cox transformation

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Fast computation of reconciled forecasts for hierarchical and grouped time series

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Measuring forecast accuracy

November 26th, 2015

Forecasting hierarchical and grouped time series through trace minimization

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Forecasting big time series data using R

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Optimal forecast reconciliation for big time series data

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Google workshop: Forecasting and visualizing big time series data

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New IJF editors

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