Forecasting at Scale

DOI10.1080/00031305.2017.1380080OpenAlexW2747599906WikidataQ58190078 ScholiaQ58190078MaRDI QIDQ5882500

Sean J. Taylor, Benjamin Letham

Publication date: 17 March 2023

Published in: The American Statistician (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/00031305.2017.1380080

zbMATH Keywords

time seriesnonlinear regressionstatistical practice

Mathematics Subject Classification ID

Statistics (62-XX)

Related Items (12)

Application of neural network to model rainfall pattern of Ethiopia ⋮ COVID-19 pandemic forecasting using CNN-LSTM: a hybrid approach ⋮ Data Aggregation and Demand Prediction ⋮ Asynchronous changepoint estimation for spatially correlated functional time series ⋮ Bayesian hierarchical models for the prediction of the driver flow and passenger waiting times in a stochastic carpooling service ⋮ Expanding the prediction capacity in long sequence time-series forecasting ⋮ Employing long short-term memory and Facebook prophet model in air temperature forecasting ⋮ Exploring hierarchical forecasting of data popularity in high-energy physics experiments ⋮ Capturing between-tasks covariance and similarities using multivariate linear mixed models ⋮ Demand forecasting of individual probability density functions with machine learning ⋮ Unnamed Item ⋮ Stock market predictions using FastRNN-based model

Uses Software

• forecast
• LBFGS-B
• Forecast
• Stan
• shiny
• prophet

Cites Work

• Unnamed Item
• An isotonic regression algorithm
• Locally Weighted Regression: An Approach to Regression Analysis by Local Fitting
• Forecasting Time Series With Complex Seasonal Patterns Using Exponential Smoothing
• Generalized Additive Models: Some Applications
• A Limited Memory Algorithm for Bound Constrained Optimization

This page was built for publication: Forecasting at Scale