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TimeSeriesWindow[tes,{tmin,tmax}]

给出时间或事件序列数据 tes 中位于 tmin 与 tmax 之间的元素.

TimeSeriesWindow[tes,winspec]

给出 tes 中满足窗口规范 winspec 的元素.

更多信息和选项
Details and Options Details and Options
范例  
基本范例  
范围  
基本用途  
数据类型  
选项  
IncludeWindowTimes  
ResamplingMethod  
CalendarType  
HolidayCalendar  
应用  
属性和关系  
参见
相关指南
历史
按以下格式引用：

TimeSeriesWindow

TimeSeriesWindow[tes,{tmin,tmax}]

给出时间或事件序列数据 tes 中位于 tmin 与 tmax 之间的元素.

TimeSeriesWindow[tes,winspec]

给出 tes 中满足窗口规范 winspec 的元素.

更多信息和选项

  • TimeSeriesWindow 用于选取落在给定的窗中的时间序列的子集.
  • TimeSeriesWindow 将选择时间戳 满足 的数据点.
  • 时间或事件序列数据 tes 的可能形式包括：
  • TimeSeries[…]连续的、按时间顺序排列的采样数据
    EventSeries[…]带有值的时间事件集合
    TemporalData[…]由时间-数值对组成的一条或多条路径
    {{t1,x1},{t2,x2},…}时间-数值对列表
    {x1,x2,…}从 0 开始的隐含整数时间对应的值列表
  • 窗口规范 winspec 可以给出为：
  • {tmin,tmax}时间或日期限制
    Interval[…]一个或多个数值时间区间
    DateInterval[…]一个或多个日历日期区间
    DateObject[_, gran]单一粒度的 DateObject
    daytype日期类型规范
  • 窗时间 tmin 和 tmax 可以以 Automatic、数字或者日期给出. 当 tmin 或者 tmax 以 Automatic 给出，分别使用 tseries 中第一个和最后一个时间.
  • 可能的 daytype 值包括 "Weekday"、"Weekend"、Monday 到 Sunday、"BeginningOfMonth"、"EndOfMonth"、"BusinessDay" 和 "Holiday".
  • TimeSeriesWindow 具有以下选项：
  • ResamplingMethod Automatic重采样路径使用的方法
    IncludeWindowTimes False如果不是时间戳成员是否在 tmin 和 tmax 创建值
    CalendarType Automatic诠释日期的日历系统
    HolidayCalendar Automatic用于工作日的假日日历安排
  • 默认情况下，只有当窗口时间 tmin 和 tmax 是 tes 的现有时间戳时，它们才会被包含在内.
  • 如果 tmin 和 tmax 超出了 tes 时间戳的范围，它们将被裁剪到该范围内.

范例

打开所有单元 关闭所有单元

基本范例  (2)

选取时间序列的子集：

Wolfram Language code: ts = TimeSeries[{1, 2, 3, 4, 5}, {DateObject[{2026, 3, 13}, "Day"]}]
Wolfram Language code: TimeSeriesWindow[ts, {DateObject[{2026, 3, 14}, "Day"], DateObject[{2026, 3, 16}, "Day"]}]
Wolfram Language code: Normal[%]

选取事件序列的子集：

Wolfram Language code: es = EventSeries[{{.1, Subscript[x, 1]}, {.6, Subscript[x, 2]}, {1.3, Subscript[x, 3]}, {2.2, Subscript[x, 4]}, {2.5, Subscript[x, 5]}}];
Wolfram Language code: TimeSeriesWindow[es, {.9, 2.3}]
Wolfram Language code: Normal[%]

范围  (7)

基本用途  (2)

从带有日期的时间序列中选取星期五：

Wolfram Language code: ts = TimeSeries[TimeEventSeries`TimestampData[Association["UniformlySpacedQ" -> False, "Timestamps" -> TabularColumn[Association[ "Data" -> {21, {{NumericArray[{15492, 15495, 15496, 15497, 15498, 15499, 15502, 15503, 15504, 15505, ... , 284.53455, 282.832828, 280.82584, 279.804813, 282.53754, 285.710728, 291.056065, 289.044041, 282.887882, 286.026035, 280.630649, 282.622623, 284.93494, 282.437443, 290.325321}, {}, None}, "ElementType" -> "Real64"]], Association[]];
Wolfram Language code: fridays = TimeSeriesWindow[ts, Friday]
Wolfram Language code: DateListPlot[{ts, fridays}, Joined -> {True, False}, Filling -> 2 -> 0, FillingStyle -> Directive[Gray, Thick]]

显示星期五的日期：

Wolfram Language code: fridays["Timestamps"]//Normal

获取相同列表的替代方法：

Wolfram Language code: DateSelect[ts["Timestamps"]//Normal, #DayName === Friday&]
Wolfram Language code: Select[ts["Timestamps"], DayMatchQ[Friday]]//Normal
Wolfram Language code: Select[ts, DayMatchQ[#Timestamp, Friday]&]
Wolfram Language code: %["Timestamps"]//Normal

可视化时间序列的不同子集：

Wolfram Language code: data = TimeSeries@RandomFunction[ARIMAProcess[{.1}, 1, {.2}, 1], {1000}]
Wolfram Language code: ListLinePlot[data]

把数据分成三部分：

Wolfram Language code: win1 = TimeSeriesWindow[data, {Automatic, 300}]; win2 = TimeSeriesWindow[data, {300, 700}]; win3 = TimeSeriesWindow[data, {700, Automatic}];
Wolfram Language code: ListLinePlot[{win1, win2, win3}, PlotLegends -> {"win1", "win2", "win3"}]

数据类型  (5)

从向量提取三个元素：

Wolfram Language code: v = {a, b, c, d, e};
Wolfram Language code: TimeSeriesWindow[v, {2, 4}]

在时间数值对的列表中选取特定的时间区间数值：

Wolfram Language code: v = {{1, a}, {3, b}, {7, c}, {14, d}, {15, e}};
Wolfram Language code: TimeSeriesWindow[v, {2, 8}]

从 TimeSeries 中提取50个数值：

Wolfram Language code: ts = TimeSeries[TimeEventSeries`TimestampData[Association["UniformlySpacedQ" -> True, "Count" -> 100, "Endpoints" -> TabularColumn[Association["Data" -> {{0, 99}, {}, None}, "ElementType" -> "Integer64"]], "MinimumTimeIncrement" -> 1, "Caller ... -5.83329995009813, -5.365194518466113, -5.614668108914484, -6.477246191853266, -7.461704439607946, -6.79657593242878, -5.864937981765857, -6.27785271209235, -5.841962111797332}, {}, None}, "ElementType" -> "Real64"]], Association[]];
Wolfram Language code: win = TimeSeriesWindow[ts, {10, 59}]

用窗函数覆盖原始数据：

Wolfram Language code: ListLinePlot[{ts, win}]

从 EventSeries 中提取30个数值：

Wolfram Language code: es = EventSeries[TimeEventSeries`TimestampData[Association["UniformlySpacedQ" -> True, "Count" -> 50, "Endpoints" -> TabularColumn[Association["Data" -> {{0, 49}, {}, None}, "ElementType" -> "Integer64"]], "MinimumTimeIncrement" -> 1, "Caller ... -1, -1, 1, 1, -1, 1, 1, -1, -1, 1, 1, 1, -1, -1, 1, -1, 1, -1, -1, -1, -1, -1, 1, -1, -1, -1, -1, 1, -1, 1, 1, 1, 1, 1, -1, 1, -1, -1, -1, 1, 1, 1, -1, 1, 1, -1, -1, -1, -1}, {}, None}, "ElementType" -> "Integer64"]], Association[]];
Wolfram Language code: win = TimeSeriesWindow[es, {10, 39}]

用窗函数覆盖原始数据：

Wolfram Language code: ListPlot[{es, win}, Filling -> Axis]

把 TemporalData 的路径限制在较小的区间：

Wolfram Language code: td = RandomFunction[WienerProcess[], {0, 20, 0.01}, 5]
Wolfram Language code: win = TimeSeriesWindow[td, {3, 15}]
Wolfram Language code: ListLinePlot[#, PlotRange -> {{0, 20}, {-10, 10}}]& /@ {td, win}

选项  (4)

IncludeWindowTimes  (1)

取一个时间-数值对列表作为时间序列数据：

Wolfram Language code: ts = {{1, 1}, {2, 2}, {3, 3}, {4, 4}};

选取时间窗端点之间的对，但不包括端点：

Wolfram Language code: TimeSeriesWindow[ts, {1.5, 3.5}]

如果端点是时间序列的时间戳，则包含它们：

Wolfram Language code: TimeSeriesWindow[ts, {2, 3}]

指定应包含窗口端点：

Wolfram Language code: TimeSeriesWindow[ts, {1.5, 3.5}, IncludeWindowTimes -> True]

仅包含右端点：

Wolfram Language code: TimeSeriesWindow[ts, {1.5, 3.5}, IncludeWindowTimes -> {False, True}]

ResamplingMethod  (1)

指定赋给窗口函数的非时间戳数值：

Wolfram Language code: s = {Subscript[x, 1], Subscript[x, 2], Subscript[x, 3], Subscript[x, 4]}; t = {2, 4, 6, 8};
Wolfram Language code: data = TimeSeries[s, {t}]; data//Normal

默认情况下，使用输入数据的重采样方法：

Wolfram Language code: win1 = TimeSeriesWindow[data, {4, 6.4}, IncludeWindowTimes -> True];
Wolfram Language code: win1//Normal//Simplify

使用阶数为1的插值：

Wolfram Language code: win2 = TimeSeriesWindow[data, {4, 6.4}, IncludeWindowTimes -> True, ResamplingMethod -> {"Interpolation", InterpolationOrder -> 1}];
Wolfram Language code: win2//Normal//Simplify

使用固定常数：

Wolfram Language code: win3 = TimeSeriesWindow[data, {4, 6.4}, ResamplingMethod -> {"Constant", c}, IncludeWindowTimes -> True];
Wolfram Language code: win3//Normal

CalendarType  (1)

使用犹太历：

Wolfram Language code: data = {{{5777, 1, 10}, 1}, {{5777, 1, 11}, 2}, {{5777, 1, 12}, 3}, {{5777, 1, 13}, 4}, {{5777, 1, 14}, 5}, {{5777, 1, 15}, 6}};
Wolfram Language code: TimeSeriesWindow[data, "Weekend", CalendarType -> "Jewish"]

HolidayCalendar  (1)

提取工作日：

Wolfram Language code: ts = TimeSeries[Range[20], {{2017, 4, 10}}];
Wolfram Language code: res = TimeSeriesWindow[ts, "BusinessDay", HolidayCalendar -> "Germany"]
Wolfram Language code: DateListPlot[{ts, res}, Joined -> False, Filling -> 2 -> 0]

应用  (3)

这个时间序列包含一个人在五个月的时间段内每天所走的步数：

Wolfram Language code: stepdata = TemporalData[TimeSeries, {{{10785, 11753, 7092, 5290, 5022, 7438, 13386, 11195, 12499, 12495, 12004, 11833, 4097, 14270, 10280, 11947, 12699, 15634, 12332, 8399, 4680, 10034, 9173, 5309, 9552, 5605, 13294, 7600, 2417, 0, 1315, 9066, 11624 ... 1, 9618, 21243, 22273, 15869, 17687, 9263, 21162, 14724, 6386}}, {TemporalData`DateSpecification[{2013, 4, 1, 0, 0, 0.}, {2013, 9, 1, 0, 0, 0.}, {1, "Day"}]}, 1, {"Discrete", 1}, {"Discrete", 1}, 1, {ValueDimensions -> 1}}, True, 10.];

显示各个月份的每日步数：

Wolfram Language code: monthlyTS = Table[TimeSeriesWindow[stepdata, DateObject[{2013, k}]], {k, 4, 8}];
Wolfram Language code: months = {"April", "May", "June", "July", "August"};DateListPlot[monthlyTS, PlotLegends -> months]

显示各个月份的总步数：

Wolfram Language code: monthlyTotals = Total /@ monthlyTS;
Wolfram Language code: PieChart[monthlyTotals, ChartLabels -> Placed[{monthlyTotals, months}, {"RadialCenter", "RadialCallout"}]]

图像化香槟地区2014年夏季的温度：

Wolfram Language code: td = TemporalData[TimeSeries, {{QuantityArray[StructuredArray`StructuredData[{365}, {{-2.94, -7.28, -14.94, -3.39, -2.56, -21, -23.06, -14.33, -6.5, 1.33, 3.22, 1.67, 5, -1.72, -2.33, -7.78, -0.5, -9, -4.28, 1.83, -0.94, -17, -12.17, -18 ... cation[{2014, 1, 1, 0, 0, 0.}, {2014, 12, 31, 0, 0, 0.}, {1, "Day"}]}, 1, {"Continuous", 1}, {"Discrete", 1}, 1, {ValueDimensions -> 1, DateFunction -> Automatic, ResamplingMethod -> {"Interpolation", InterpolationOrder -> 1}}}, True, 10.1];

提取夏季月份的温度：

Wolfram Language code: summer = TimeSeriesWindow[td, {DateObject[{2014, 6, 1}, "Day", "Gregorian", -6.], DateObject[{2014, 9, 1}, "Day", "Gregorian", -6.]}]

找到基本描述性统计：

Wolfram Language code: {Min[#], Mean[#], Max[#]}&@summer
Wolfram Language code: DateListPlot[{td, summer}, FrameLabel -> Automatic]

提取时间序列的一部分：

Wolfram Language code: ts = TimeSeries[FinancialData["GOOGL", {"1 Jan 2012", "1 Jan 2014"}]]
Wolfram Language code: DateListPlot[ts]

选取 2012 年 5 月 4 日至 8 月 4 日之间的元素：

Wolfram Language code: win = TimeSeriesWindow[ts, {{2012, 5, 4}, {2012, 8, 4}}]
Wolfram Language code: DateListPlot[win]

提取 2012 年 6 月的元素：

Wolfram Language code: TimeSeriesWindow[ts, DateObject[{2012, 6}, "Month"]]

提取每月末的元素：

Wolfram Language code: end = TimeSeriesWindow[ts, "EndOfMonth"]
Wolfram Language code: DateListPlot[end, PlotMarkers -> Automatic]

属性和关系  (1)

Take 根据一段索引范围提取时间序列中的元素：

Wolfram Language code: ts = TimeSeries[Range[10], {DateObject[{2026, 1, 21}]}]
Wolfram Language code: Take[ts, {3, 6}]

TimeSeriesWindow 使用时间范围：

Wolfram Language code: TimeSeriesWindow[ts, {DateObject[{2026, 1, 23}], DateObject[{2026, 1, 26}]}]

这些日期对应于先前使用的索引范围 {3,6}:

Wolfram Language code: ts["Times"][[{3, 6}]]//Normal
Wolfram Research (2014)，TimeSeriesWindow，Wolfram 语言函数，https://reference.wolfram.com/language/ref/TimeSeriesWindow.html （更新于 2026 年）.

文本

Wolfram Research (2014)，TimeSeriesWindow，Wolfram 语言函数，https://reference.wolfram.com/language/ref/TimeSeriesWindow.html （更新于 2026 年）.

CMS

Wolfram 语言. 2014. "TimeSeriesWindow." Wolfram 语言与系统参考资料中心. Wolfram Research. 最新版本 2026. https://reference.wolfram.com/language/ref/TimeSeriesWindow.html.

APA

Wolfram 语言. (2014). TimeSeriesWindow. Wolfram 语言与系统参考资料中心. 追溯自 https://reference.wolfram.com/language/ref/TimeSeriesWindow.html 年

BibTeX

@misc{reference.wolfram_2026_timeserieswindow, author="Wolfram Research", title="{TimeSeriesWindow}", year="2026", howpublished="\url{https://reference.wolfram.com/language/ref/TimeSeriesWindow.html}", note=[Accessed: 13-November-2026]}

BibLaTeX

@online{reference.wolfram_2026_timeserieswindow, organization={Wolfram Research}, title={TimeSeriesWindow}, year={2026}, url={https://reference.wolfram.com/language/ref/TimeSeriesWindow.html}, note=[Accessed: 13-November-2026]}