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第1行： − 此词条由地球系统科学读书会词条梳理志愿者（李柔静）翻译审校，未经专家审核，带来阅读不便，请见谅。 − {{Use American English|date = March 2019}} − + + + − 在数学Mathematics中，时间序列Time series是按时间顺序索引(或列出或绘制)的一系列数据点Data point。最常见的是，时间序列是在连续的等距时间点上的序列Sequence。因此，它是一个离散时间Discrete-time数据的序列。时间序列的例子有海洋潮汐Tides的高度、太阳黑子Sunspot的数量以及道琼斯工业平均指数Dow Jones Industrial Average的每日收盘价。 − 时间序列经常通过趋势图Run chart（时间线图Line chart）来绘制。时间序列被用于统计学Statistics、信号处理Signal processing、模式识别Pattern recognition、计量经济学Econometrics、数理金融学Mathematical finance、天气预报Weather forecasting、地震预测Earthquake prediction、脑电图Electroencephalography、控制工程Control engineering、天文学Astronomy、通信工程Communications engineering，以及涉及时序Temporal测量的任何应用科学Science和工程Engineering领域。+ 第26行：
第25行： − Time series analysis can be applied to real-valued, continuous data, discrete numeric data, or discrete symbolic data (i.e. sequences of characters, such as letters and words in the English language). − 时间序列分析可以应用于实值real-valued、连续数据、离散Discrete数值Numeric数据或离散符号数据（即字符序列，如英语English language中的字母和单词<ref name=":0" />）。+
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{{short description|Sequence of data points over time}}
{{short description|Sequence of data points over time}}
[[File:Random-data-plus-trend-r2.png|thumb|250px|Time series: random data plus trend, with best-fit line and different applied filters时间序列：随机数据加趋势，带有最佳拟合线和不同的过滤器|right|链接=Special:FilePath/Random-data-plus-trend-r2.png]]
[[File:Random-data-plus-trend-r2.png|thumb|250px|Time series: random data plus trend, with best-fit line and different applied filters时间序列：随机数据加趋势，带有最佳拟合线和不同的过滤器|right|链接=Special:FilePath/Random-data-plus-trend-r2.png]]
In [[mathematics]], a '''time series''' is a series of [[data point]]s indexed (or listed or graphed) in time order. Most commonly, a time series is a [[sequence]] taken at successive equally spaced points in time. Thus it is a sequence of [[discrete-time]] data. Examples of time series are heights of ocean [[tides]], counts of [[sunspots]], and the daily closing value of the [[Dow Jones Industrial Average]].
In [[mathematics]], a '''time series''' is a series of [[data point]]s indexed (or listed or graphed) in time order. A time series is a [[sequence]] taken at successive equally spaced points in time. Thus it is a sequence of [[discrete-time]] data. Examples of time series are heights of ocean [[tides]], counts of [[sunspots]], and the daily closing value of the [[Dow Jones Industrial Average]].
数学中的时间序列是指按时间顺序索引(或列出或绘制)的一系列数据点。时间序列是在连续的等距时间点上的序列。因此，这种序列上的时间是处于离散状态的。测量海洋潮汐的高度、计算太阳黑子的数量和分析道琼斯工业平均指数的每日收盘价都是时间序列在实际工作上的应用。
A Time series is very frequently plotted via a [[run chart]] (which is a temporal [[line chart]]). Time series are used in [[statistics]], [[signal processing]], [[pattern recognition]], [[econometrics]], [[mathematical finance]], [[weather forecasting]], [[earthquake prediction]], [[electroencephalography]], [[control engineering]], [[astronomy]], [[communications engineering]], and largely in any domain of applied [[Applied science|science]] and [[engineering]] which involves [[Time|temporal]] measurements.
A Time series is very frequently plotted via a [[run chart]] (which is a temporal [[line chart]]). Time series are used in [[statistics]], [[signal processing]], [[pattern recognition]], [[econometrics]], [[mathematical finance]], [[weather forecasting]], [[earthquake prediction]], [[electroencephalography]], [[control engineering]], [[astronomy]], [[communications engineering]], and largely in any domain of applied [[Applied science|science]] and [[engineering]] which involves [[Time|temporal]] measurements.
时间序列常通过趋势图（即时间线图Line chart）具象化。时间序列常被用于统计学、信号处理、模式识别、计量经济学、数理金融学、天气预报、地震预测、脑电图、控制工程、天文学、通信工程，以及涉及时序测量的任何科学和工程领域。
Time series analysis can be applied to [[real number|real-valued]], continuous data, [[:wikt:discrete|discrete]] [[Data type#Numeric types|numeric]] data, or discrete symbolic data (i.e. sequences of characters, such as letters and words in the [[English language]]<ref name=":0">{{cite book |last1=Lin |first1=Jessica |last2=Keogh |first2=Eamonn |last3=Lonardi |first3=Stefano |last4=Chiu |first4=Bill |chapter=A symbolic representation of time series, with implications for streaming algorithms |title=Proceedings of the 8th ACM SIGMOD workshop on Research issues in data mining and knowledge discovery |pages=2–11 |year=2003 |location=New York |publisher=ACM Press |doi=10.1145/882082.882086|citeseerx=10.1.1.14.5597 |s2cid=6084733 }}</ref>).
Time series analysis can be applied to [[real number|real-valued]], continuous data, [[:wikt:discrete|discrete]] [[Data type#Numeric types|numeric]] data, or discrete symbolic data (i.e. sequences of characters, such as letters and words in the [[English language]]<ref name=":0">{{cite book |last1=Lin |first1=Jessica |last2=Keogh |first2=Eamonn |last3=Lonardi |first3=Stefano |last4=Chiu |first4=Bill |chapter=A symbolic representation of time series, with implications for streaming algorithms |title=Proceedings of the 8th ACM SIGMOD workshop on Research issues in data mining and knowledge discovery |pages=2–11 |year=2003 |location=New York |publisher=ACM Press |doi=10.1145/882082.882086|citeseerx=10.1.1.14.5597 |s2cid=6084733 }}</ref>).
时间序列分析可以应用于实值、连续数据、离散数值Numeric数据或离散符号数据（即字符序列，如英语English language中的字母和单词<ref name=":0" />）。
==Methods for analysis分析方法==
==Methods for analysis分析方法==