title: how to improve prophet model
body: <p>I am running the following R code:</p>
  
  <pre><code>library(readxl)
  library(prophet)
  df &lt;- read_excel("20151001-20180531 Data.xlsx", 
                                        col_types = c("date", "numeric", "numeric", 
                                                      "numeric", "numeric"))
  h=365
  prodf=data.frame(ds=df$Transaction_Date[-((nrow(df)-(h-1)):nrow(df))],
                   y=log(df$`Volume Demand`[-((nrow(df)-(h-1)):nrow(df))]),
                   gasessions=log(df$Sessions[-((nrow(df)-(h-1)):nrow(df))]),
                   avgprice=log(df$`Avg RRP`[-((nrow(df)-(h-1)):nrow(df))]),
                   discdepth=df$`Avg Discount`[-((nrow(df)-(h-1)):nrow(df))])
  
  # allholidays=data.frame(holiday="allholidays",
  #                     ds=as.Date(c("2015-10-31","2015-11-26","2015-11-27","2015-11-30","2015-12-18","2015-12-24","2015-12-25","2015-12-26","2015-12-31",
  #                          "2016-01-01","2016-02-14","2016-03-25","2016-10-31","2016-11-24","2016-11-25","2016-11-28","2016-12-18","2016-12-24","2016-12-25","2016-12-26","2016-12-31",
  #                          "2017-01-01","2017-02-14","2017-04-14","2017-10-31","2017-11-23","2017-11-24","2017-11-27","2017-12-16","2017-12-24","2017-12-25","2017-12-26","2017-12-31",
  #                          "2018-01-01","2018-02-14","2018-04-1")),
  #                     lower_window = 0,
  #                     upper_window = 1, stringsAsFactors = F)
  calendar &lt;- read_excel("Calendar.xlsx", 
                   col_types = c("text", "date", "numeric", "numeric"))
  m=prophet(holidays = calendar, holidays.prior.scale = 10,changepoint.prior.scale = 0.05)#,mcmc.samples = 300)
  m &lt;- add_seasonality(m, name='weekly', period=7, fourier.order=5, prior.scale=10)
  m &lt;- add_seasonality(m, name='yearly', period=365, fourier.order=5, prior.scale=10)
  m &lt;- add_regressor(m, 'gasessions')
  m &lt;- add_regressor(m, 'avgprice')
  m &lt;- add_regressor(m, 'discdepth')
  m &lt;- fit.prophet(m, prodf)
  future &lt;- make_future_dataframe(m, periods = h)
  future$gasessions=log(df$Sessions)
  future$avgprice=log(df$`Avg RRP`)
  future$discdepth=df$`Avg Discount`
  forecast &lt;- predict(m, future)
  MAPE=mean(abs(tail(exp(forecast$yhat),h)-tail(df$`Volume Demand`,h))/tail(df$`Volume Demand`,h))*100
  accuracy=((sum(tail(exp(forecast$yhat),h))-sum(tail(df$`Volume Demand`,h)))/sum(tail(df$`Volume Demand`,h)))*100
  TotForecast=sum(tail(exp(forecast$yhat),h))
  </code></pre>
  
  <p>I am trying to predict next year sales. Unfortunately I cannot provide the data for obvious reasons. MAPE is 19% and accuracy is 27%. These are my best results for 365 days horizon. I have achieved 0.5% accuracy (99.5% correct prediction) with different settings for a horizon of 28 days but 365 days horizon accuracy is worse than the aforementioned (probably overfitting). Anyone has a suggestion on improving this prophet model? Also, if there is a way to transform data so that they are shareable then please let me know.
  Kind Regards
  George</p>