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: $ 1.75万
• 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: 2017
• 资助国家: 加拿大
• 起止时间: 2017-01-01 至 2018-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=635816
• 关键词: Time; Series; Models; Sparsity; Mis

A large quantity of data is collected at regular time intervals such as stock prices or weather measurements of various types. Often inter-related measurements at for the same time unit are available such as with daily stock open, close, high and low prices and such data are examples of multiple time series. Sometimes the spatial dimension also provides important information and space-time statistical models are required.

定期收集大量数据，例如股票价格或各种类型的天气测量数据。通常可以使用同一时间单位的相互关联的测量，例如每日股票开盘价、收盘价、最高价和最低价，并且此类数据是多个时间序列的示例。有时空间维度也提供了重要信息，需要时空统计模型。