Time series forecast

forecast.R

library(forecast)
library(ggplot2)
library(dplyr)

predictions = as.factor(read.csv('results.csv')$Prediction)
levels(predictions) <- c("ok", "drop")

submissions <- read.csv('submissions.csv')$Submissions
submissions <- msts(submissions, seasonal.periods=c(24, 7*24), ts.frequency=24)

df <- data.frame(submissions) %.%
  mutate(time = 1:n()/24,
         prediction = predictions)

ggplot(aes(x = time, y = submissions), data=df) + geom_point() + xlab('Days') + ylab('Submissions') + theme_bw()
ggplot(aes(x = time, y = submissions), data=df) + geom_line() + xlab('Days') + ylab('Submissions') + theme_bw()
ggplot(aes(x = time, y = submissions), data=df) + geom_point(aes(color = prediction)) + xlab('Days') + ylab('Submissions') + theme_bw()

classic_decomp <- decompose(ts(submissions, deltat=1/24))
bt <- tbats(submissions, use.parallel=T, num.cores=8)

submissions.clean <- window(submissions, start=20, end=40)
submissions.clean.test <- window(submissions, start=40.01, end=45)
bt.clean <- tbats(submissions.clean, use.parallel=T, num.cores=8)

df <- data.frame(submissions=submissions.clean.test) %.%
  mutate(prediction = (forecast(bt.clean, h=5*24)$mean),
         time = 1:n()/24)

ggplot(aes(time), data=df) + geom_line(aes(y = submissions, colour = 'Submissions')) + 
  geom_line(aes(y = prediction, color = 'Predictions')) + 
  scale_colour_manual(values=c("red", "black"))

results.csv

Prediction
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submissions.csv

Submissions
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