Forecasting small area populations with long short-term memory networks
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DOI: 10.1016/j.seps.2023.101658
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- Tom Wilson & Fiona Shalley, 2019. "Subnational population forecasts: Do users want to know about uncertainty?," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 41(13), pages 367-392.
- Mathew E. Hauer & Jason M. Evans & Deepak R. Mishra, 2016. "Millions projected to be at risk from sea-level rise in the continental United States," Nature Climate Change, Nature, vol. 6(7), pages 691-695, July.
- Spyros Makridakis & Evangelos Spiliotis & Vassilios Assimakopoulos, 2018. "Statistical and Machine Learning forecasting methods: Concerns and ways forward," PLOS ONE, Public Library of Science, vol. 13(3), pages 1-26, March.
- Peng, Lu & Wang, Lin & Xia, De & Gao, Qinglu, 2022. "Effective energy consumption forecasting using empirical wavelet transform and long short-term memory," Energy, Elsevier, vol. 238(PB).
- Wilson, Thomas & Grossman, Irina & Alexander, Monica & Rees, Philip & Temple, Jeromey, 2021. "Methods for small area population forecasts: state-of-the-art and research needs," SocArXiv sp6me, Center for Open Science.
- Sean J. Taylor & Benjamin Letham, 2018. "Forecasting at Scale," The American Statistician, Taylor & Francis Journals, vol. 72(1), pages 37-45, January.
- Heather Booth & Rob Hyndman & Leonie Tickle & Piet de Jong, 2006.
"Lee-Carter mortality forecasting: a multi-country comparison of variants and extensions,"
Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 15(9), pages 289-310.
- Heather Booth & Rob J Hyndman & Leonie Tickle & Piet de Jong, 2006. "Lee-Carter mortality forecasting: a multi-country comparison of variants and extensions," Monash Econometrics and Business Statistics Working Papers 13/06, Monash University, Department of Econometrics and Business Statistics.
- Andrea Nigri & Susanna Levantesi & Mario Marino, 2021. "Life expectancy and lifespan disparity forecasting: a long short-term memory approach," Scandinavian Actuarial Journal, Taylor & Francis Journals, vol. 2021(2), pages 110-133, February.
- Tom Wilson & Huw Brokensha & Francisco Rowe & Ludi Simpson, 2018. "Insights from the Evaluation of Past Local Area Population Forecasts," Population Research and Policy Review, Springer;Southern Demographic Association (SDA), vol. 37(1), pages 137-155, February.
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Keywords
Population forecasts; Small area populations; Machine learning; Long short-term memory; Australia;All these keywords.
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