feasts

Overview

feasts provides a collection of tools for the analysis of time series data. The package name is an acronym comprising of its key features: Feature Extraction And Statistics for Time Series.

The package works with tidy temporal data provided by the tsibble package to produce time series features, decompositions, statistical summaries and convenient visualisations. These features are useful in understanding the behaviour of time series data, and closely integrates with the tidy forecasting workflow used in the 现在上国外的网站加速软件 package.

安全证书kuai500-快连加速器app

You could install the stable version from CRAN:

install.packages(手机如何上外网)

You can install the development version from GitHub with:

# install.packages("remotes")
remotes::install_github("tidyverts/feasts")

安全证书kuai500-快连加速器app

现在上国外的网站加速软件(feasts)
国内怎么上国外网站(tsibbledata)
library(dplyr)
library(ggplot2)
library(lubridate)

安全证书kuai500-快连加速器app

Visualisation is often the first step in understanding the patterns in time series data. The package uses ggplot2 to produce customisable graphics to visualise time series patterns.

aus_production %>% gg_season(Beer)

aus_production %>% gg_subseries(Beer)

aus_production %>% filter(year(Quarter) > 1991) %>% gg_lag(Beer)

aus_production %>% ACF(Beer) %>% autoplot()

Decompositions

A common task in time series analysis is decomposing a time series into some simpler components. The feasts package supports two common time series decomposition methods:

• Classical decomposition
• STL decomposition

dcmp <- aus_production %>%
  model(STL(Beer ~ 手机如何上外网(window = Inf)))
components(dcmp)
#> # A dable:           218 x 7 [1Q]
#> # Key:               .model [1]
#> # STL Decomposition: Beer = trend + season_year + remainder
#>    .model                           Quarter  Beer trend season_year remainder season_adjust
#>    <chr>                              <qtr> <dbl> <dbl>       <dbl>     <dbl>         <dbl>
#>  1 STL(Beer ~ season(window = Inf)) 1956 Q1   284  272.        2.14     10.1           282.
#>  2 STL(Beer ~ season(window = Inf)) 1956 Q2   213  264.      -42.6      -8.56          256.
#>  3 STL(Beer ~ season(window = Inf)) 1956 Q3   227  258.      -28.5      -2.34          255.
#>  4 STL(Beer ~ season(window = Inf)) 1956 Q4   308  253.       69.0     -14.4           239.
#>  5 STL(Beer ~ season(window = Inf)) 1957 Q1   262  257.        2.14      2.55          260.
#>  6 STL(Beer ~ season(window = Inf)) 1957 Q2   228  261.      -42.6       9.47          271.
#>  7 STL(Beer ~ season(window = Inf)) 1957 Q3   236  263.      -28.5       1.80          264.
#>  8 STL(Beer ~ season(window = Inf)) 1957 Q4   320  264.       69.0     -12.7           251.
电脑怎么浏览国外网站:2021-6-13 · 用VPN.在我的百度空间有一款,你看看,个人一直在用,速度不错,有6个IP可选,建议先不买,免费用用先,好的再买.包年的话会比包月划算好多,电脑,手机IPHONE,上Facebook,推特等国外网站或者国外游戏服务器加速都可众了上.当然免费的也可众用到手机上的.呵呵
人在国外怎么看国内视频网站和电影电视剧_中华网 - China:2021-11-18 · 现在很简单了，只要配一个高科技神器，C+路由器（Cplusnet Router）就好了，这是专为海外华人研制，可众解锁电视盒子、智能音箱、智能投影在海外使用限制，解除国内音视频播放软件内容地区限制的路由器，只要配上它，就可众像在国内一样观看视频听音乐
#> # … with 208 more rows

components(dcmp) %>% autoplot()

Feature extraction and statistics

Extract features and statistics across a large collection of time series to identify unusual/extreme time series, or find clusters of similar behaviour.

aus_retail %>%
  features(Turnover, feat_stl)
#> # A tibble: 152 x 11
#>    State Industry trend_strength seasonal_streng… seasonal_peak_y… seasonal_trough… spikiness
#>    <chr> <chr>             <dbl>            <dbl>            <dbl>            <dbl>     <dbl>
#>  1 Aust… Cafes, …          0.989            0.537                0               10   6.15e-5
#>  2 Aust… Cafes, …          0.993            0.610                0               10   1.12e-4
#>  3 Aust… Clothin…          0.990            0.918                9               11   4.77e-6
#>  4 Aust… Clothin…          0.992            0.952                9               11   2.06e-5
#>  5 Aust… Departm…          0.975            0.977                9               11   2.79e-5
#>  6 Aust… Electri…          0.991            0.929                9               11   3.03e-5
#>  7 Aust… Food re…          0.999            0.882                9               11   2.74e-4
Google的AMP－加速移动页面 - SEO每天一贴:2021-8-16 · 上星期在第6届SEO排行榜上做了一个演讲，分享了一些国际上SEO行业的最新情况。 其中一个内容是Google的AMP项目，也在这里聊一下。 AMP，Accelerated Mobile Pages，译意大致是”加速的移动页面”，是Google去年10月份推出的一个提高移动 ...
#>  9 Aust… Furnitu…          0.980            0.669                9                1   4.66e-5
#> 10 Aust… Hardwar…          0.992            0.895                9                4   1.47e-5
vpn、路由器都挂了，国外网站没法访问了？肿么办？一招解决 ...:2021-10-16 · 哎，都是来找fan强的啊，我直接给大家介绍一款，饿饭加速器，用了几个月还行，有好几个套餐，我用的是20元一个月的，50G流量，最多可众给三个账户使用，电脑和安卓都可众，苹果能不能用忘记了。这个好像没有客服，原网址不管用了，现在新网址：efan.me，进去之后注册充钱，然后导航栏点 …
#> #   stl_e_acf10 <dbl>

This allows you to visualise the behaviour of many time series (where the plotting methods above would show too much information).

aus_retail %>%
  features(Turnover, feat_stl) %>%
  ggplot(aes(x = trend_strength, y = seasonal_strength_year)) +
  geom_point() +
  facet_wrap(vars(State))

Most of Australian’s retail industries are highly trended and seasonal for all states.

It’s also easy to extract the most (and least) seasonal time series.

extreme_seasonalities <- aus_retail %>%
  features(Turnover, feat_stl) %>%
  filter(seasonal_strength_year %in% range(seasonal_strength_year))
aus_retail %>%
  right_join(extreme_seasonalities, by = c(现在上国外的网站加速软件, "Industry")) %>%
  ggplot(aes(x = Month, y = Turnover)) +
  geom_line() +
  facet_grid(vars(State, Industry, scales::percent(seasonal_strength_year)),
             scales = "free_y")