Esame Pepe Novelli-Prevedere prezzo Bitcoin con Prophet

options(lifecycle_verbosity = "quiet")

library(prophet)

## Caricamento del pacchetto richiesto: Rcpp

## Caricamento del pacchetto richiesto: rlang

library(tidyverse)

## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr     1.1.4     ✔ readr     2.1.6
## ✔ forcats   1.0.1     ✔ stringr   1.6.0
## ✔ ggplot2   4.0.1     ✔ tibble    3.3.1
## ✔ lubridate 1.9.4     ✔ tidyr     1.3.2
## ✔ purrr     1.2.1

## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter()      masks stats::filter()
## ✖ purrr::flatten()     masks rlang::flatten()
## ✖ purrr::flatten_chr() masks rlang::flatten_chr()
## ✖ purrr::flatten_dbl() masks rlang::flatten_dbl()
## ✖ purrr::flatten_int() masks rlang::flatten_int()
## ✖ purrr::flatten_lgl() masks rlang::flatten_lgl()
## ✖ purrr::flatten_raw() masks rlang::flatten_raw()
## ✖ purrr::invoke()      masks rlang::invoke()
## ✖ dplyr::lag()         masks stats::lag()
## ✖ purrr::splice()      masks rlang::splice()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

library(readxl)

data<-read_excel("C:/Users/lele/OneDrive/Desktop/APSS/data set bit.xlsx",sheet=1)

view(data)

data$ds<-as.Date(data$ds)
data$y<-as.numeric(data$y)

Model<-prophet(yearly.seasonality = TRUE,weekly.seasonality = TRUE,daily.seasonality = FALSE,changepoint.prior.scale = 0.5)
Model<-add_country_holidays(Model,country_name = 'US')
Model<-fit.prophet(Model,data)

Future<-make_future_dataframe(Model,periods=365,freq="day")

tail(Future)

##              ds
## 3430 2027-01-07
## 3431 2027-01-08
## 3432 2027-01-09
## 3433 2027-01-10
## 3434 2027-01-11
## 3435 2027-01-12

Forecast<-predict(Model,Future)
tail(Forecast[c('ds','yhat','yhat_lower','yhat_upper')])

##              ds     yhat yhat_lower yhat_upper
## 3430 2027-01-07 145019.7   94019.05   195631.1
## 3431 2027-01-08 145182.0   95412.39   195341.1
## 3432 2027-01-09 145377.3   96102.92   196035.7
## 3433 2027-01-10 145622.2   93933.89   195436.3
## 3434 2027-01-11 145837.5   93817.77   197255.7
## 3435 2027-01-12 146068.9   94153.45   196663.5

dyplot.prophet(Model,Forecast)

prophet_plot_components(Model,Forecast)