最近又接到一个预测项目需求，主要是预测每天投资用户会投资不同产品多少金额，属于每天即时预测，需要拿最近一年的数据做测试集，来预测每天不同期限产品分别被投资多少金额，然后通过这些金额每天找借款端匹配借款需求，借款端运营通过资金端需求来动态调整营销活动，通过资金需求多少来有效运营借款需求，形成与资金端的良性互动，节约闲置资金成本。
就这样一个需求，如何实现？我首先想到通过业务经验的方法来实现，需要从N多产品中抽丝剥茧，找到产品、推广，促销，节日之间的关系，最终确定一个期限的产品当天成交预测值；但是从成交数据来看，这些数据都是每天在变化的，不能明显看出两者之间有明显的相关关系，用这些现有的数据关系能预测将来的成交吗？
既然是预测问题，那我首先想到的是时序预测，那么问题就来了，如何程序化这些现有的数据关系？
在网上找了一些相关模型，并没有模型来实现我所说的推广，促销，节日，加息，降息等业务时点与预测数据的关系，仔细查找，首先（注意还有后续，我会在下篇博文做出解释）发现了一个名叫prophet的时序预测包，里面可以用不同类型的holiday值来对模型进行业务时点的匹配和修正，非常适合目前的项目需求。不管三七二十一，先安装了再说。
安装了之后需要初始化数据，初始化节假日，初始化模型，调整参数，衡量结果，具体代码如下：
library(prophet)
library(dplyr) #初始化数据 all<-read.csv(‘d:/Rdata/zjd/ts/all.csv’,na.string=’NA’,header=T)
alln<-read.csv(‘d:/Rdata/zjd/ts/alln.csv’,na.string=’NA’,header=T)
qb30<-read.csv(‘d:/Rdata/zjd/ts/qb30.csv’,na.string=’NA’,header=T)
qb45<-read.csv(‘d:/Rdata/zjd/ts/qb45.csv’,na.string=’NA’,header=T)
qb60<-read.csv(‘d:/Rdata/zjd/ts/qb60.csv’,na.string=’NA’,header=T)
qb90<-read.csv(‘d:/Rdata/zjd/ts/qb90.csv’,na.string=’NA’,header=T)
qb180<-read.csv(‘d:/Rdata/zjd/ts/qb180.csv’,na.string=’NA’,header=T)
qb270<-read.csv(‘d:/Rdata/zjd/ts/qb270.csv’,na.string=’NA’,header=T)
qb365<-read.csv(‘d:/Rdata/zjd/ts/qb365.csv’,na.string=’NA’,header=T)
qb730<-read.csv(‘d:/Rdata/zjd/ts/qb730.csv’,na.string=’NA’,header=T)
qb1095<-read.csv(‘d:/Rdata/zjd/ts/qb1095.csv’,na.string=’NA’,header=T)
qb1095m<-read.csv(‘d:/Rdata/zjd/ts/qb1095+.csv’,na.string=’NA’,header=T)
qbother<-read.csv(‘d:/Rdata/zjd/ts/qbother.csv’,na.string=’NA’,header=T)
#holiday<-read.csv(‘d:/Rdata/zjd/ts/holidays.csv’,na.string=’NA’,header=T)
historyalln <- data.frame(ds = seq(as.Date(‘2017-01-01’), as.Date(‘2017-09-17’), by = ‘d’), y = alln$yn)
historyalln <- data.frame(ds = seq(as.Date(‘2016-01-01’), as.Date(‘2017-09-11’), by = ‘d’), y = all$yn)
historyallo <- data.frame(ds = seq(as.Date(‘2016-01-01’), as.Date(‘2017-09-11’), by = ‘d’), y = all$yo)
history30n <- data.frame(ds = seq(as.Date(‘2016-01-01’), as.Date(‘2017-09-11’), by = ‘d’), y = qb30$yn)
plot(history30n$ds,history30n$y)
history45n <- data.frame(ds = seq(as.Date(‘2016-01-01’), as.Date(‘2017-09-11’), by = ‘d’), y = qb45$yn)
history60n <- data.frame(ds = seq(as.Date(‘2016-01-01’), as.Date(‘2017-09-11’), by = ‘d’), y = qb60$yn)
history90n <- data.frame(ds = seq(as.Date(‘2016-01-01’), as.Date(‘2017-09-11’), by = ‘d’), y = qb90$yn)
history180n <- data.frame(ds = seq(as.Date(‘2016-01-01’), as.Date(‘2017-09-11’), by = ‘d’), y = qb180$yn)
history270n <- data.frame(ds = seq(as.Date(‘2016-01-01’), as.Date(‘2017-09-11’), by = ‘d’), y = qb270$yn)
history365n <- data.frame(ds = seq(as.Date(‘2016-01-01’), as.Date(‘2017-09-11’), by = ‘d’), y = qb365$yn)
history730n <- data.frame(ds = seq(as.Date(‘2016-01-01’), as.Date(‘2017-09-11’), by = ‘d’), y = qb730$yn)
history1095n <- data.frame(ds = seq(as.Date(‘2016-01-01’), as.Date(‘2017-09-11’), by = ‘d’), y = qb1095$yn)
history1095m <- data.frame(ds = seq(as.Date(‘2016-01-01’), as.Date(‘2017-09-11’), by = ‘d’), y = qb1095m$yn)
historyothern <- data.frame(ds = seq(as.Date(‘2016-01-01’), as.Date(‘2017-09-11’), by = ‘d’), y = qbother$yn)
history30o <- data.frame(ds = seq(as.Date(‘2016-01-01’), as.Date(‘2017-09-11’), by = ‘d’), y = qb30$yo)
history45o <- data.frame(ds = seq(as.Date(‘2016-01-01’), as.Date(‘2017-09-11’), by = ‘d’), y = qb45$yo)
history60o <- data.frame(ds = seq(as.Date(‘2016-01-01’), as.Date(‘2017-09-11’), by = ‘d’), y = qb60$yo)
history90o <- data.frame(ds = seq(as.Date(‘2016-01-01’), as.Date(‘2017-09-11’), by = ‘d’), y = qb90$yo)
history180o <- data.frame(ds = seq(as.Date(‘2016-01-01’), as.Date(‘2017-09-11’), by = ‘d’), y = qb180$yo)
history270o <- data.frame(ds = seq(as.Date(‘2016-01-01’), as.Date(‘2017-09-11’), by = ‘d’), y = qb270$yo)
history365o <- data.frame(ds = seq(as.Date(‘2016-01-01’), as.Date(‘2017-09-11’), by = ‘d’), y = qb365$yo)
history730o <- data.frame(ds = seq(as.Date(‘2016-01-01’), as.Date(‘2017-09-11’), by = ‘d’), y = qb730$yo)
history1095o <- data.frame(ds = seq(as.Date(‘2016-01-01’), as.Date(‘2017-09-11’), by = ‘d’), y = qb1095$yo)
history1095p <- data.frame(ds = seq(as.Date(‘2016-01-01’), as.Date(‘2017-09-11’), by = ‘d’), y = qb1095m$yo)
historyothero <- data.frame(ds = seq(as.Date(‘2016-01-01’), as.Date(‘2017-09-11’), by = ‘d’), y = qbother$yo) #初始化节假日
holiday <- data_frame(
holiday = ‘holiday’,
ds = as.Date(c( ‘2017-01-01′,’2017-01-27’, ‘2017-04-02′,’2017-04-29’,
‘2017-05-28’)),
lower_window = 0,
upper_window = 2
)
payday <- data_frame(
holiday = ‘payday’,
ds = as.Date(c( ‘2016-01-26’,
‘2016-02-26′,’2016-03-26’, ‘2016-04-26′,’2016-05-26’,
‘2016-06-26′,’2016-07-26’, ‘2016-08-26′,’2016-09-26’,
‘2016-10-26′,’2016-11-26’, ‘2016-12-26′,’2017-01-26’,
‘2017-02-26′,’2017-03-26’, ‘2017-04-26′,’2017-05-26’,
‘2017-06-26′,’2017-07-26’, ‘2017-08-26′,’2017-09-26’)),
lower_window = 0,
upper_window = 1
)
VIP <- data_frame(
holiday = ‘VIP’,
ds = as.Date(c(
‘2016-06-09′,’2016-07-09’, ‘2016-08-09′,’2016-09-09’,
‘2016-10-09′,’2016-11-09’, ‘2016-12-09′,’2017-01-09’,
‘2017-02-09′,’2017-03-09’, ‘2017-04-09′,’2017-05-09’,
‘2017-06-09’, ‘2017-07-09’, ‘2017-08-09’, ‘2017-09-09’)),
lower_window = 0,
upper_window = 1
)
raiserate <- data_frame(
holiday = ‘raiserate’,
ds = as.Date(c( ‘2017-05-08′,’2017-06-19’, ‘2017-07-24’)),
lower_window = 0,
upper_window = 7
)
reducerate <- data_frame(
holiday = ‘reducerate’,
ds = as.Date(c(‘2016-06-20′,’2017-02-22’, ‘2017-03-27’, ‘2017-04-05’)),
lower_window = 0,
upper_window = 7
)
newcustomer <- data_frame(
holiday = ‘newcustomer’,
ds = as.Date(c(‘2016-01-01′,’2016-01-15′,’2016-07-18′,’2016-08-24’,
‘2016-09-07′,’2016-12-09′,’2016-12-22′,’2017-01-06′,’2017-04-12’)),
lower_window = 0,
upper_window = 7
)
promotion <- data_frame(
holiday = ‘promotion’,
ds = as.Date(c(‘2016-07-07′,’2016-11-03′,’2017-01-10’, ‘2017-01-17’, ‘2017-02-10’)),
lower_window = 0,
upper_window = 7
)
holidays<-bind_rows(holiday,payday,VIP, raiserate,reducerate,newcustomer,promotion) #初始化模型 nall <- prophet(historyalln,
holidays = holidays,
seasonality.prior.scale=5,
changepoint.prior.scale=0.9,
holidays.prior.scale = 20,
mcmc.samples = 500,
interval.width=0.9,
uncertainty.samples = 30)
oall <- prophet(historyallo,
holidays = holidays,
seasonality.prior.scale=5,
changepoint.prior.scale=0.1,
holidays.prior.scale = 1,
mcmc.samples = 100,
interval.width=0.9,
uncertainty.samples = 100)
n30 <- prophet(history30n,
holidays = holidays,
seasonality.prior.scale=5,
changepoint.prior.scale=0.1,
holidays.prior.scale = 1,
mcmc.samples = 100,
interval.width=0.9,
uncertainty.samples = 100)
n45 <- prophet(history45n,holidays = holidays, holidays.prior.scale = 1, mcmc.samples = 500)
n60 <- prophet(history60n,holidays = holidays, holidays.prior.scale = 1, mcmc.samples = 500)
n90 <- prophet(history90n,holidays = holidays, holidays.prior.scale = 1, mcmc.samples = 500)
n180 <- prophet(history180n,holidays = holidays, holidays.prior.scale = 1, mcmc.samples = 500)
n270 <- prophet(history270n,holidays = holidays, holidays.prior.scale = 1, mcmc.samples = 500)
n365 <- prophet(history365n,holidays = holidays, holidays.prior.scale = 1, mcmc.samples = 500)
n730 <- prophet(history730n,holidays = holidays, holidays.prior.scale = 1, mcmc.samples = 500)
n1095 <- prophet(history1095n,holidays = holidays, holidays.prior.scale = 1, mcmc.samples = 500)
n1095m <- prophet(history1095m,holidays = holidays, holidays.prior.scale = 1, mcmc.samples = 500)
nother <- prophet(historyothern,holidays = holidays, holidays.prior.scale = 1, mcmc.samples = 500)
o30 <- prophet(history30o,holidays = holidays, holidays.prior.scale = 1, mcmc.samples = 500)
o45 <- prophet(history45o,holidays = holidays, holidays.prior.scale = 1, mcmc.samples = 500)
o60 <- prophet(history60o,holidays = holidays, holidays.prior.scale = 1, mcmc.samples = 500)
o90 <- prophet(history90o,holidays = holidays, holidays.prior.scale = 1, mcmc.samples = 500)
o180 <- prophet(history180o,holidays = holidays, holidays.prior.scale = 1, mcmc.samples = 500)
o270 <- prophet(history270o,holidays = holidays, holidays.prior.scale = 1, mcmc.samples = 500)
o365 <- prophet(history365o,holidays = holidays, holidays.prior.scale = 1, mcmc.samples = 500)
o730 <- prophet(history730o,holidays = holidays, holidays.prior.scale = 1, mcmc.samples = 500)
o1095 <- prophet(history1095o,holidays = holidays, holidays.prior.scale = 1, mcmc.samples = 500)
o1095p <- prophet(history1095p,holidays = holidays, holidays.prior.scale = 1, mcmc.samples = 500)
oother <- prophet(historyothero,holidays = holidays, holidays.prior.scale = 1, mcmc.samples = 500) #设定预测时间 futurealln <- make_future_dataframe(nall, periods = 7)
futureallo <- make_future_dataframe(oall, periods = 7)
future30n <- make_future_dataframe(n30, periods = 7)
future45n <- make_future_dataframe(n45, periods = 7)
future60n <- make_future_dataframe(n60, periods = 7)
future90n <- make_future_dataframe(n90, periods = 7)
future180n <- make_future_dataframe(n180, periods = 7)
future270n <- make_future_dataframe(n270, periods = 7)
future365n <- make_future_dataframe(n365, periods = 7)
future730n <- make_future_dataframe(n730, periods = 7)
future1095n <- make_future_dataframe(n1095, periods = 7)
future1095m <- make_future_dataframe(n1095m, periods = 7)
futureothern <- make_future_dataframe(nother, periods = 7)
future30o <- make_future_dataframe(o30, periods = 7)
future45o <- make_future_dataframe(o45, periods = 7)
future60o <- make_future_dataframe(o60, periods = 7)
future90o <- make_future_dataframe(o90, periods = 7)
future180o <- make_future_dataframe(o180, periods = 7)
future270o <- make_future_dataframe(o270, periods = 7)
future365o <- make_future_dataframe(o365, periods = 7)
future730o <- make_future_dataframe(o730, periods = 7)
future1095o <- make_future_dataframe(o1095, periods = 7)
future1095p <- make_future_dataframe(o1095p, periods = 7)
futureothero <- make_future_dataframe(oother, periods = 7)
tail(future30n)
#预测
forecastalln <- predict(nall, futurealln)
forecastallo <- predict(oall, futureallo)
forecast30n <- predict(n30, future30n)
forecast45n <- predict(n45, future45n)
forecast60n <- predict(n60, future60n)
forecast90n <- predict(n90, future90n)
forecast180n <- predict(n180, future180n)
forecast270n <- predict(n270, future270n)
forecast365n <- predict(n365, future365n)
forecast730n <- predict(n730, future730n)
forecast1095n <- predict(n1095, future1095n)
forecast1095m <- predict(n1095m, future1095m)
forecastothern <- predict(nother, futureothern)
forecast30o <- predict(o30, future30o)
forecast45o <- predict(o45, future45o)
forecast60o <- predict(o60, future60o)
forecast90o <- predict(o90, future90o)
forecast180o <- predict(o180, future180o)
forecast270o <- predict(o270, future270o)
forecast365o <- predict(o365, future365o)
forecast730o <- predict(o730, future730o)
forecast1095o <- predict(o1095, future1095o)
forecast1095p <- predict(o1095p, future1095p)
forecastothero <- predict(oother, futureothero)
# tail(forecast90n[c(‘ds’, ‘yhat’, ‘yhat_lower’, ‘yhat_upper’)]) forecastalln %>%
select(ds,payday,VIP, raiserate,reducerate,newcustomer,promotion) %>%
filter(abs(payday+VIP+raiserate+reducerate+newcustomer+promotion)>0) %>%
tail(10) forecastallo %>%
select(ds,payday,VIP, raiserate,reducerate,newcustomer,promotion) %>%
filter(abs(payday+VIP+raiserate+reducerate+newcustomer+promotion)>0) %>%
tail(10) #直线预测
plot(nall, forecastalln)
plot(n30, forecast30n)
plot(oall, forecastallo)
#趋势分解
prophet_plot_components(nall, forecastalln)
prophet_plot_components(oall, forecastallo) a<-cbind(futurealln[1],forecastalln$yhat,forecast30n$yhat,forecast45n$yhat,forecast60n$yhat,forecast90n$yhat,forecast180n$yhat,forecast270n$yhat,forecast365n$yhat,forecast730n$yhat,forecast1095n$yhat,forecast1095m$yhat,forecastothern$yhat)
b<-cbind(futureallo[1],forecastallo$yhat,forecast30o$yhat,forecast45o$yhat,forecast60o$yhat,forecast90o$yhat,forecast180o$yhat,forecast270o$yhat,forecast365o$yhat,forecast730o$yhat,forecast1095o$yhat,forecast1095p$yhat,forecastothero$yhat)
write.csv(a,file = “d:/Rdata/zjd/ts/qballn.csv”)
write.csv(b,file = “d:/Rdata/zjd/ts/qballo.csv”)
目前预测准确率总金额能较为准确的预测，调整好参数后总成交金额差值约为1%左右；不同产品期限成交金额预测会有10%上下的波动。 参考文献：http://blog.csdn.net/sinat_26917383/article/details/57419862 http://blog.csdn.net/justinjia91/article/details/62470076
