library(TSstudio)
library(tidyverse)typeof(USgas)## [1] "double"class(USgas)## [1] "ts"USgas## Jan Feb Mar Apr May Jun Jul Aug Sep Oct
## 2000 2510.5 2330.7 2050.6 1783.3 1632.9 1513.1 1525.6 1653.1 1475.0 1567.8
## 2001 2677.0 2309.5 2246.6 1807.2 1522.4 1444.4 1598.1 1669.2 1494.1 1649.1
## 2002 2487.6 2242.4 2258.4 1881.0 1611.5 1591.4 1748.4 1725.7 1542.2 1645.9
## 2003 2700.5 2500.3 2197.9 1743.5 1514.7 1368.4 1600.5 1651.6 1428.6 1553.2
## 2004 2675.8 2511.1 2100.9 1745.2 1573.0 1483.7 1584.9 1578.0 1482.2 1557.2
## 2005 2561.9 2243.0 2205.8 1724.9 1522.6 1534.1 1686.6 1695.1 1422.5 1428.2
## 2006 2165.3 2144.4 2126.4 1681.0 1526.3 1550.9 1758.7 1751.7 1462.1 1644.2
## 2007 2475.6 2567.0 2128.8 1810.1 1559.1 1555.2 1659.9 1896.1 1590.5 1627.8
## 2008 2734.0 2503.4 2278.2 1823.9 1576.4 1604.2 1708.6 1682.9 1460.9 1635.8
## 2009 2729.7 2332.5 2170.7 1741.3 1504.0 1527.8 1658.0 1736.5 1575.0 1666.5
## 2010 2809.8 2481.0 2142.9 1691.8 1617.3 1649.5 1825.8 1878.9 1637.5 1664.9
## 2011 2888.6 2452.4 2230.5 1825.0 1667.4 1657.3 1890.5 1891.8 1655.6 1744.5
## 2012 2756.2 2500.7 2127.8 1953.1 1873.8 1868.4 2069.8 2008.8 1807.2 1901.1
## 2013 2878.8 2567.2 2521.1 1967.5 1752.5 1742.9 1926.3 1927.4 1767.0 1866.8
## 2014 3204.1 2741.2 2557.9 1961.7 1810.2 1745.4 1881.0 1933.1 1809.3 1912.8
## 2015 3115.0 2925.2 2591.3 2007.9 1858.1 1899.9 2067.7 2052.7 1901.3 1987.3
## 2016 3091.7 2652.3 2356.3 2083.8 1965.8 2000.7 2186.6 2208.4 1947.8 1925.2
## 2017 2914.2 2340.6 2523.7 1932.5 1892.5 1910.9 2142.1 2094.3 1920.9 2032.0
## 2018 3335.0 2705.9 2792.6 2346.3 2050.9 2058.7 2344.6 2307.7 2151.5 2279.1
## 2019 3399.9 2999.2 2899.9 2201.1 2121.0 2115.2 2407.5 2437.2 2215.6 2472.3
## Nov Dec
## 2000 1908.5 2587.5
## 2001 1701.0 2120.2
## 2002 1913.6 2378.9
## 2003 1753.6 2263.7
## 2004 1782.8 2327.7
## 2005 1663.4 2326.4
## 2006 1765.4 2122.8
## 2007 1834.5 2399.2
## 2008 1868.9 2399.7
## 2009 1776.2 2491.9
## 2010 1973.3 2714.1
## 2011 2031.9 2541.9
## 2012 2167.8 2503.9
## 2013 2316.9 2920.8
## 2014 2357.5 2679.2
## 2015 2249.1 2588.2
## 2016 2159.4 2866.3
## 2017 2357.7 3084.5
## 2018 2709.9 2993.1
## 2019注意 input 的格式。
Input object, either a univariate time series object of a class “ts”, “zoo”, “xts”, or a data frame object of a class “data.frame”, “tbl”, “data.table” as long as there is at least one “Date”/“POSIXt” and a “numeric” objects
已经其他时间格式的数据。
ts_seasonal(USgas, type = "all")## Warning: `arrange_()` is deprecated as of dplyr 0.7.0.
## Please use `arrange()` instead.
## See vignette('programming') for more help
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_warnings()` to see where this warning was generated.这里使用箱型图，更好表现了季节性是否显著，因此可以认为这个包不错。
ts_lags(USgas, lags = 1:12)这样查询 lag 记录，可以给sense 可以应用到 sales 项目。
ts_heatmap(USgas)这里研究除了周期、lag之外，其他规律的探查。