R Notebook

ex_item_domain_id_train <- train_data_meta2 %>% filter(item_domain_id=="SNEAKERS", country=="MLM") %>% group_by(date) %>% summarise(y=sum(sold_quantity))


ex_item_domain_id_val <- val_data_meta %>% filter(item_domain_id=="SNEAKERS", country=="MLM") %>% group_by(date) %>% summarise(y=sum(sold_quantity))

ex_item_domain_id_val<-ex_item_domain_id_val %>% mutate(ds=ymd(date))
ex_item_domain_id_train<-ex_item_domain_id_train %>% mutate(ds=ymd(date))
ex_item_domain_id_train

library(prophet)
m<-prophet(ex_item_domain_id_train)

Disabling yearly seasonality. Run prophet with yearly.seasonality=TRUE to override this.
Disabling daily seasonality. Run prophet with daily.seasonality=TRUE to override this.
n.changepoints greater than number of observations. Using 22

future <- make_future_dataframe(m, periods = 30)

future %>% tail()

forecast <- predict(m, future)
tail(forecast[c('ds', 'yhat', 'yhat_lower', 'yhat_upper')])

NA

plot(m,forecast)+
  #ggplot(ex_item_domain_id_train)+
  geom_line(aes(x=as.POSIXct(ds),y=y),color='red',data=ex_item_domain_id_train,linetype = 2)+
 geom_line(aes(x=as.POSIXct(ds),y=y),data=ex_item_domain_id_val,color='white',linetype = 2)+
  ggdark::dark_theme_bw()+
  geom_vline(xintercept=as.POSIXct(dmy("01-03-2021")),color='orange',size=1)+
  labs(title="prophet: SNEAKERS demand forecast", subtitle = "RED line train data, WHITE line validation data, BLUE, prophet prediction")

m<-prophet(ex_sku_id_train)

Disabling yearly seasonality. Run prophet with yearly.seasonality=TRUE to override this.
Disabling daily seasonality. Run prophet with daily.seasonality=TRUE to override this.
n.changepoints greater than number of observations. Using 22

future <- make_future_dataframe(m, periods = 30)
future %>% tail()

forecast <- predict(m, future)

plot(m,forecast)+
  #ggplot(ex_item_domain_id_train)+
  geom_line(aes(x=as.POSIXct(ds),y=y),color='red',data=ex_sku_id_train,linetype = 2)+
 geom_line(aes(x=as.POSIXct(ds),y=y),data=ex_sku_id_val,color='white',linetype = 2)+
  ggdark::dark_theme_bw()+
  geom_vline(xintercept=as.POSIXct(dmy("01-03-2021")),color='orange',size=1)+
  labs(title="prophet: SNEAKERS demand forecast", subtitle = "RED line train data, WHITE line validation data, BLUE, prophet prediction")