比较人工神经网络和队列成分模型在人口预测中的应用

IF 0.4 Q4 DEMOGRAPHY Population Review Pub Date : 2019-10-22 DOI:10.1353/prv.2019.0008
Viktoria Riiman, Amalee Wilson, Reed Milewicz, P. Pirkelbauer
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引用次数: 9

摘要

摘要:人工神经网络(ANN)模型虽然在其他领域越来越突出,但很少用于人口预测。我们将人工神经网络长短期记忆模型(LSTM)与传统队列成分法(CCM)的人口预测结果进行了比较。评估包括对所有67个县的预测,这些县的人口和社会经济特征各不相同。当将预测值与2010年十年一次人口普查的总人口进行比较时,2001年阿拉巴马大学商业和经济研究中心使用的CCM比基本的多县ANN LSTM模型产生了相当或更好的结果。当我们使用单一国家模型或代理预测者的经验和个人判断与潜在的经济预测时,人工神经网络模型的结果会得到改善。结果表明预报员的经验/判断对CCM的重要性,以及用现有数据替代这些见解的难度,但并非不可能。
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Comparing Artificial Neural Network and Cohort-Component Models for Population Forecasts
Abstract:Artificial neural network (ANN) models are rarely used to forecast population in spite of their growing prominence in other fields. We compare the forecasts generated by ANN long short-term memory models (LSTM) with population projections from the traditional cohort-component method (CCM) for counties in Alabama, USA. The evaluation includes projections for all 67 counties, which are diverse in population and socioeconomic characteristics. When comparing projected values with total population counts from the 2010 decennial census, the CCM used by the Center for Business and Economic Research at the University of Alabama in 2001 produced comparable or better results than a basic multi-county ANN LSTM model. Results from ANN models improve when we use single-county models or proxy for a forecaster’s experience and personal judgment with potential economic forecasts. The results indicate the significance of forecaster’s experience/judgment for CCM and the difficulty, but not impossibility, of substituting these insights with available data.
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来源期刊
Population Review
Population Review DEMOGRAPHY-
CiteScore
1.30
自引率
14.30%
发文量
3
期刊介绍: Population Review publishes scholarly research that covers a broad range of social science disciplines, including demography, sociology, social anthropology, socioenvironmental science, communication, and political science. The journal emphasizes empirical research and strives to advance knowledge on the interrelationships between demography and sociology. The editor welcomes submissions that combine theory with solid empirical research. Articles that are of general interest to population specialists are also desired. International in scope, the journal’s focus is not limited by geography. Submissions are encouraged from scholars in both the developing and developed world. Population Review publishes original articles and book reviews. Content is published online immediately after acceptance.
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