# Why the seasonality of daily time series is not predicted correctly in R with arima model?

I have a question related to the estimation of arima models in R. I have estimated a model with daily simulated data where Mondays have a lower value than the rest of the days. I have simulated two years of data, and I want to predict the next year.

According to the data, I should obtain an estimation which has lower values on Mondays, but I get it on Tuesdays... It seems the data has been desplaced one day. I show my code.

library(ggplot2)
library(forecast)
library(lubridate)

date <- seq(as.Date('2018-01-01'),as.Date('2019-12-31'),by='day')
orders <- rnorm(length(date),100,10)
s <- seq(1,length(date),by=7)
for(i in s)
{
orders[i] <- orders[i]-rnorm(1,70,10)
}

datos <- data.frame(date = date,orders = orders)

ggplot()+
geom_line(data = datos, aes(x = date, y = orders))


datos begins on Monday 2018-01-01. Every Monday has a lower value. Now I estimate the model with the corresponding seasonal periods.

orders_ts <- msts(datos\$orders,seasonal.periods = c(7,365.25),start = decimal_date(as.Date("2018-01-01")))

model <- auto.arima(orders_ts,trace = T,D=1)
pred <- forecast(model,h=365)
plot(pred)

pred_tabla <- as.data.frame(pred)
date_pred <- as.Date(time(rownames(pred_tabla)), origin = "2019-12-31")
rownames(pred_tabla) <- date_pred
pred_tabla <- dplyr::select(pred_tabla,c('Point Forecast'))
colnames(pred_tabla) <- 'Prediction'

Prediction
2020-01-01   89.65607
2020-01-02  127.10014
2020-01-03  104.69489
2020-01-04  104.82119
2020-01-05   90.43497
2020-01-06  119.54629
2020-01-07   28.30212
2020-01-08   76.84848
2020-01-09   89.47474
2020-01-10  108.30905
2020-01-11  108.61325
2020-01-12   79.79830
2020-01-13   82.68802
2020-01-14   30.47466
2020-01-15   83.57336
2020-01-16  104.82201
2020-01-17   94.73705
2020-01-18   97.66296
2020-01-19   94.98595
2020-01-20   99.00874
2020-01-21   43.39133
2020-01-22  110.69250
2020-01-23   99.88973
2020-01-24   91.25167
2020-01-25   86.29674
2020-01-26   99.76741
2020-01-27   96.38425
2020-01-28   17.60269
2020-01-29   99.22064
2020-01-30  116.89777
2020-01-31  114.49706
2020-02-01  108.64221
2020-02-02  114.64886
2020-02-03   88.54560
2020-02-04   17.62015
2020-02-05  101.96455
2020-02-06   97.18976
2020-02-07  100.91465
2020-02-08   94.81816
2020-02-09  103.15029
2020-02-10  100.13627
2020-02-11   32.48818
2020-02-12   97.34060
2020-02-13   99.69957
2020-02-14   82.02583
2020-02-15   89.17326
2020-02-16   93.99973
2020-02-17   98.01702
2020-02-18   34.04982
2020-02-19   91.95284


Now the lower values are on Tuesdays, in 2020-01-07, 2020-01-14, etc.

Could someone give me an answer of why this is happening? And how could I solve this?

Thank you very much!