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What is Recursive Forecasting Strategy

Handbook of Research on Foundations and Applications of Intelligent Business Analytics
Uses a one-step model multiple times in which the prediction for the prior time step feeds into the next time step for prediction.
Published in Chapter:
A Comparison of Deep Learning Models in Time Series Forecasting of Web Traffic Data From Kaggle
Bingnan Wang (The University of Hong Kong, Hong Kong), Dickson K. W. Chiu (The University of Hong Kong, Hong Kong), and Kevin K. W. Ho (University of Guam, Guam)
DOI: 10.4018/978-1-7998-9016-4.ch014
Abstract
In recent years, time series forecasting has attracted more attention from academia and industry. This research used raw data from the “Web Traffic Forecasting” competition on the Kaggle platform to test the prediction accuracy of different time series models, especially the generalization performance of various deep learning models. The experiments used historical traffic data from 145,063 web pages from Wikipedia from 2015-07-01 to 2017-11-13. Traffic data from 2015-07-01 to 2017-09-10 was used to forecast traffic from 2017-09-13 to 2017-11-13, a total of 62 days. The experimental results showed that almost all deep learning models predicted far more effectively than statistical and machine learning models, showing that deep learning models have great potential for time series forecasting problems.
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