UC知道英译中！！！！！！！！！！机器翻译的勿进
来源：百度知道 编辑：UC知道 时间：2024/06/30 17:42:01
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.
In fitting an ARIMA model to time series data the framework usually used is a Box-Jenkins approach. It does however have the disadvantage that whereas a number of time series techniques are fully automatic, in the sense that the forecaster has t
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.
In fitting an ARIMA model to time series data the framework usually used is a Box-Jenkins approach. It does however have the disadvantage that whereas a number of time series techniques are fully automatic, in the sense that the forecaster has t
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. 在一个ARIMA模型拟合时间序列数据是常用的框架箱詹金斯的办法. 不过有不少缺点,而时序技术全自动、 在意识到预报已行使任何判决以外,在选择使用技术、 箱詹金斯技术要求作出判断和预报,需要运用经验,因此它和"专家判断"的 部分的预报. 预报方案确实存在一些令这些"专家的选择"给你.
加先进的时间数列预测时间数列预测方法被推进比那些被考虑在我们简单的包裹里存在。这些根据自回归联合移动平均数(ARIMA) 模型。这些根本上假设, 时间数列由一个可能性过程引起了以将来值与过去价值有关, 并且对过去展望错误。运用时间数列需要固定式的ARIMA 模型。一个固定式时间数列是一个统计物产譬如手段、变化和自相关是恒定的随时间。如果最初的时间数列不固定式它也许是, 某一时间数列的作用, 即采取区别在连续价值之间, 固定式。 在适合一个ARIMA 模型对时间数列数据框架通常被使用是一种箱子Jenkins 方法。它然而有的不利但是一定数量的时间数列技术是充分地自动的, 在感觉预测员不必须行使评断除在选择技术之外使用, 箱子Jenkins 技术要求预测员做评断并且它的用途因而要求经验和"专家的评断" 在预测员部分。做出这些"专家的选择" 为您的一些预测包裹存在。
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