Time Series Models: Sparsity, Mis-specification and Forecasting

时间序列模型：稀疏性、错误指定和预测

• 批准号: RGPIN-2017-06082
• 负责人: McLeod, AngusIan
• 金额: $ 3.5万
• 依托单位: University of Western Ontario
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2021
• 资助国家: 加拿大
• 起止时间: 2021-01-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=734718
• 关键词: Time; Series; Models; Sparsity; Mis

Bayesian hiearchical spatio-temporal models; clustering and classification of time series; forecasting volatility; microarray time series experiments and periodicity tests; multivariate time series; optimal forecasts for transformed series; sparsity, mis-specification and prediction; statistical computing; testing for whiteness in weakly dependent time series; time series analysis, model building, forecasting

贝叶斯分层时空模型；时间序列的聚类和分类；预测波动性；微阵列时间序列实验和周期性测试；多元时间序列；变换序列的最优预测；稀疏性、错误指定和预测；统计计算；在弱相关时间序列中测试白度；时间序列分析、模型构建、预测