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.

以固定的时间间隔收集大量数据，如股票价格或各种类型的天气测量。通常可以对同一时间单位进行相互关联的测量，例如每日股票开盘、收盘、高价和低价，这些数据是多时间序列的例子。有时空间维度也提供重要的信息，需要时空统计模型。