A state space framework for automatic forecasting using exponential smoothing methods

2002/07/01 by Rob J. Hyndman, Rob J Hyndman, Anne B Koehler +5 · 12 citations
Computer Science · Decision Sciences · #Forecasting Techniques and Applications #Stock Market Forecasting Methods #Time Series Analysis and Forecasting

paper · doi:10.1016/s0169-2070(01)00110-8

openalex publication_date 2002/07/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/01

Citations

• The M3-Competition: results, conclusions and implications

Cited by

• TimePre: Bridging Accuracy, Efficiency, and Stability in Probabilistic Time-Series Forecasting
• ARIMA_PLUS: Large-scale, Accurate, Automatic and Interpretable In-Database Time Series Forecasting and Anomaly Detection in Google BigQuery
• Optimal smoothing parameter in Eilers-Whittaker smoother
• Comparative Analysis of Global and Local Probabilistic Time Series Forecasting for Contiguous Spatial Demand Regions
• Machine-learning forecasting model of tuberculosis cases among children in South Africa
• Leveraging the Christoffel Function for Outlier Detection in Data Streams
• Forecasting age distribution of deaths across countries: Life expectancy and annuity valuation
• Hybrid Modeling Techniques for Municipal Solid Waste Forecasting: An Application to OECD Countries
• The effects of retraining on the stability of global models in retail demand forecasting
• Another look at measures of forecast accuracy
• The M4 Competition: 100,000 time series and 61 forecasting methods
• Intelligent Routing for Sparse Demand Forecasting: A Comparative Evaluation of Selection Strategies