R手册(Time Series)–zoo,forecast and prophet
zoo
Part 1 基础对象
zoo(x = NULL, order.by = index(x), frequency = NULL)
有序的时间序列对象
zooreg(data, start = 1, end = numeric(), frequency = 1)
规则的的时间序列对象,继承zoo对象
as.zoo(x)
把一个对象转型为zoo类型,泛型函数
is.regular(x, strict = FALSE)
检查是否是规则的序列
Part 2 ggplot2扩展
autoplot(object, geom = "line", facets, ...)
fortify(model, data, melt = FALSE, …)
数据操作
Part 3 数据清洗
read.zoo(file, format = "", index.column = 1, drop = TRUE)
write.zoo(x, file = "", index.name = "Index", row.names = FALSE, col.names = NULL)
coredata(x)
提取/替换zoo数据部分
index(x)
提取/替换zoo索引部分
window(x, index. = index(x), start = NULL, end = NULL)
按时间筛选数据
merge()
合并多个zoo对象
aggregate(x, by, FUN = sum)
分类计算
lag(x, k = 1, na.pad = FALSE, ...)
计算步长
diff(x, lag = 1, differences = 1)
计算分差
rollapply(data, width, FUN)
对zoo数据的滚动处理
rollmean, rollmax, rollmedian, rollsum,etc
MATCH(x, table)
值匹配
ORDER(x)
值排序,输出索引
Part 4 缺失值处理
na.fill(object, fill)
NA值的填充
na.locf(object, na.rm = TRUE, fromLast=FALSE)
最近值替换NA
na.aggregate(object,by = 1,FUN = mean,na.rm = FALSE)
计算统计值替换NA
na.approx(object)
计算插值替换NA
na.StructTS(object,na.rm=FALSE)
计算seasonalKalmanfilter替换NA
na.trim(object)
过滤有NA的记录
Part 5 显示控制
yearqtr
以年季度显示时间
yearmon
以年月显示时间
forecast : for Time Series and Linear Models
Part 1 时间序列分析
tsclean(x, replace.missing = TRUE, lambda = NULL)
识别和替换异常值和缺失值(lambda给出Box-Cox变换参数的数值)
ndiffs, nsdiffs
固定系列所需的差异数
seasonal(object)
提取季节分量
trendcycle(object)
提取趋势周期分量
remainder(object)
提取余数分量
findfrequency
查找时间序列的主频
ma(x, order, centre = TRUE)
计算更平滑的移动平均
Part 2 模型
arfima
: FitARFIMAmodel
Arima, auto.arima
: FitARIMAmodel
ets(y,model=”ZZZ”)
指数预测模型
baggedETS, bats, tbats
: FitbaggedETS/BATS/TBATSmodel
nnetar
神经网络时间序列预测
forecastHybrid: 组合模型
hybridModel(y, models = "aefnst", #模型组a(auto.arima),e(ets),f(thetam),n(nnetar),s(stlm),t(tbats)
weights = c("equal", "insample.errors", "cv.errors"), #模型加权方法
parallel = FALSE, #是否并行运算
num.cores = 2L) #并行内核数
example:
hybridModel(wineind, models = "aet", weights = "equal")%>%
forecast(hm1, h = 48)%>%plot()
Part 3 预测
forecast(object, h = ifelse(frequency(object) > 1, 2*frequency(object), 10),level=c(80,95))
参数:
h:预测数
level:置信区间
ggplot2扩展
Acf, Pacf, Ccf, taperedacf, taperedpacf
自相关和相关函数估计
autoplot(object, …)
通用制图函数
Part 4 评估
checkresiduals(object, lag, df = NULL, test, plot = TRUE, ...)
检查残差
accuracy(f, x)
准确率
CV, CVar, dsCV
交叉验证
dm.test
Diebold-Mariano测试的预测准确性
example: WWWusage %>%auto.arima %>%forecast(h=20) %>%autoplot()
prophet
—-模型组成:Y(t)=Trend(t)+Seasonal(t)+Holiday(t)+Irregular(t)
Part 1 构建模型
prophet(df = df, #data.frame:ds(date type)+ y,cap+floor指定饱和最大值和最小值
growth = "linear", #linearorlogistictrend
changepoints = NULL, #包含潜在变化点的日期向量
n.changepoints = 25, #潜在变化点数
changepoint_prior_scale=0.05, #调整trend灵活性
yearly.seasonality = "auto", #适合年度seasonality;'auto',TRUE,orFALSE
weekly.seasonality = "auto", #适合周度
holidays = NULL, #data.frame:holiday(character)+ds(datetype),lower_window+upper_window(可选,指定假日周围的天数)
seasonality.prior.scale = 10, #调整季节性模型的强度
holidays.prior.scale = 10, #调整假期组件模型的强度
mcmc.samples = 0,
interval.width = 0.8, #trend间隔不确定性
uncertainty.samples = 1000, #season的不确定习性
fit = TRUE)
Part 2 模型预测
furture<-make_future_dataframe(m, #Prophet model object|
periods, #要预测的数量
freq = "d", #day','week','month','quarter',or'year'
include_history = TRUE) #历史日期是否包含在预测中
predict(object,df = NULL)
参数:
object:Prophet modelo bject
df:NULL or future
Part 3 可视化
plot(x, fcst)
prophet_plot_components(m, fcst)
ggplot2组件,将预测细分为趋势,每周季节性和年度季节
Part 4 交叉验证
cross_validation(m, horizon ,initial,units = 'days')
参数horizon, initial, units:初始日期,截止日期,间隔