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来源：百度知道 编辑：UC知道 时间：2024/05/12 16:09:13
More advanced time series forecasting
Time series forecasting methods more advanced than those considered in our simple package do exist. These are based on AutoRegressive Integrated Moving Average (ARIMA) models. Essentially these assume that the time series has been generated by a probability process with future values related to past values, as well as to past forecast errors. To apply ARIMA models the time series needs to be stationary. A stationary time series is one whose statistical properties such as mean, variance and autocorrelation are constant over time. If the initial time series is not stationary it may be that some function of the time series, e.g. taking the differences between successive values, is stationary.
怎么读着怪怪的.
好象你的than翻译的不太通顺
Time series forecasting methods more advanced than those considered in our simple package do exist. These are based on AutoRegressive Integrated Moving Average (ARIMA) models. Essentially these assume that the time series has been generated by a probability process with future values related to past values, as well as to past forecast errors. To apply ARIMA models the time series needs to be stationary. A stationary time series is one whose statistical properties such as mean, variance and autocorrelation are constant over time. If the initial time series is not stationary it may be that some function of the time series, e.g. taking the differences between successive values, is stationary.
怎么读着怪怪的.
好象你的than翻译的不太通顺
更先进的时间序列预测时间序列预测方法更先进,比我们认为确实存在简单包装. 这些都是基于自综合移动平均模型(ARIMA模型). 基本上,这些假设时序已经产生了概率过程与过去与未来的价值观 价值观,以及过去预测误差. 申请时间序列ARIMA模型需要加以固定. 一个平稳时间序列的统计特性等,是指,在一段时间内不断变异和自. 如果时间序列不是最初可能是一些固定的时间序列功能,例如 参加历届价值观差异,是静止.