# How to forecast correctly the seasonality in R?

Consider the following code:

library(forecast)
library(lubridate)

# Create random walk with growing tendency and decreasing tendency after 182 days
a <- c(rnorm(182, 0, 100), rnorm(204, 0, 100))
b <- c(seq(1000, 3000, length.out = 182), seq(3000, 1000, length.out = 204))
y <- a + b
date <- seq(as.Date('2019-01-01'), as.Date('2020-01-21'), by='day')
data <- data.frame(date = date, y = y)

# Plot data
ggplot()+
geom_line(data = data, aes(x = date, y = y))

# I want to consider yearly and weekly seasonal periods
datats <- msts(data\$y, seasonal.periods = c(365.25, 7), start = decimal_date(as.Date(date[1])))

# Create arima model with seasonal differences
model <- auto.arima(datats, max.d = 1, D = 1, seasonal.test = 'ch')

# Plot forecast
plot(forecast(model, 100))


The prediction of this model is really bad. I would like a prediction which decreases as the recent days, but at the same time it takes into account the anual seasonality. I don't know how to do this, because it seems that the model don't keep the tendency. Obviously, this artificial data has an exaggerated peak. But what happens if I have a data that has a growing tendency, and then it decreases? Will I be able to keep the recent tendency and consider the year seasonality as well?

Thank you very much.