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第74行： − In [[statistics]], [[prediction]] is a part of [[statistical inference]]. One particular approach to such inference is known as [[predictive inference]], but the prediction can be undertaken within any of the several approaches to statistical inference. Indeed, one description of statistics is that it provides a means of transferring knowledge about a sample of a population to the whole population, and to other related populations, which is not necessarily the same as prediction over time. When information is transferred across time, often to specific points in time, the process is known as [[forecasting]].+ − + − * Simple or fully formed statistical models to describe the likely outcome of the time series in the immediate future, given knowledge of the most recent outcomes (forecasting). − * Forecasting on time series is usually done using automated statistical software packages and programming languages, such as [[Julia (programming language)|Julia]], [[Python (programming language)|Python]], [[R (programming language)|R]], [[SAS (software)|SAS]], [[SPSS]] and many others. − * Forecasting on large scale data can be done with [[Apache Spark]] using the Spark-TS library, a third-party package.<ref>{{cite web |title=Time Series Analysis with Spark |author=Sandy Ryza |date=2020-03-18 |access-date=2021-01-12 |url=https://databricks.com/session/time-series-analysis-with-spark |format=slides of a talk at Spark Summit East 2016 |publisher=[[Databricks]]}}</ref> − − In statistics, prediction is a part of statistical inference. One particular approach to such inference is known as predictive inference, but the prediction can be undertaken within any of the several approaches to statistical inference. Indeed, one description of statistics is that it provides a means of transferring knowledge about a sample of a population to the whole population, and to other related populations, which is not necessarily the same as prediction over time. When information is transferred across time, often to specific points in time, the process is known as forecasting. − * Fully formed statistical models for stochastic simulation purposes, so as to generate alternative versions of the time series, representing what might happen over non-specific time-periods in the future − * Simple or fully formed statistical models to describe the likely outcome of the time series in the immediate future, given knowledge of the most recent outcomes (forecasting). − * Forecasting on time series is usually done using automated statistical software packages and programming languages, such as Julia, Python, R, SAS, SPSS and many others. − * Forecasting on large scale data can be done with Apache Spark using the Spark-TS library, a third-party package. − − 在统计学中，预测是推论统计学的一部分。一种特殊的推理方法被称为预测推理，但是这种预测可以在几种推论统计学推理方法中的任何一种中进行。事实上，对统计的一种描述是，它提供了一种将关于某一人口样本的知识转移给整个人口和其他相关人口的手段，这不一定与随着时间的推移所作的预测相同。当信息跨越时间传递，通常是传递到特定的时间点，这个过程就被称为预测。 − * 为随机模拟目的而建立完整的统计模型，以产生时间序列的替代版本，反映未来在非特定时间段内可能发生的情况 − * 简单或完整的统计模型，以描述时间序列在最近期间可能产生的结果(预测)。
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===时间序列分析的预测功能===
===时间序列分析的预测功能===
在统计学中，预测是统计学的推理环节的一部分。有一种推理方法是预测推理，这种预测可以与几种统计学推理方法混合使用。统计学的预测方法之一是将部分样本数值扩大到整体去分析。这不一定与随着时间的推移所作的预测相同。当信息跨越时间传递，通常是传递到特定的时间点，推测特定时间点信息的状态的个过程就被称为预测。
* Fully formed statistical models for [[stochastic simulation]] purposes, so as to generate alternative versions of the time series, representing what might happen over non-specific time-periods in the future
* 为完成随机模拟而建立完整的统计模型能产生时间序列的替代版本，会反映未来在非特定时间段内可能发生的情况。
* 时间序列预测通常使用自动化的统计软件包和编程语言，例如 Julia、 Python、 r、 SAS、 SPSS 等。
* 时间序列预测通常使用自动化的统计软件包和编程语言，例如 Julia、 Python、 r、 SAS、 SPSS 等。
* 使用第三方软件包 Spark-TS 库，Apache Spark 可以对大规模数据进行预测。
* 使用第三方软件包 Spark-TS 库，Apache Spark 可以对大规模数据进行预测。