Kordyuk_Lab_4_Report

library(tidyverse)
library(tsibble)
library(ggplot2)
library(lubridate)
library(readxl)
library(gridExtra)
library(dplyr)
library(patchwork)
library(zoo)
library(fable)
library(feasts)
library(fabletools)

tesla <- as_tsibble(read_csv("TSLA.csv"), index = Date)%>%
  glimpse()

Rows: 965
Columns: 7
$ Date        <date> 2019-01-02, 2019-01-03, 2019-01-04, 2019-01-07, 2019-01-0…
$ Open        <dbl> 20.40667, 20.46667, 20.40000, 21.44800, 22.79733, 22.36667…
$ High        <dbl> 21.00867, 20.62667, 21.20000, 22.44933, 22.93400, 22.90000…
$ Low         <dbl> 19.92000, 19.82533, 20.18200, 21.18333, 21.80133, 22.09800…
$ Close       <dbl> 20.67467, 20.02400, 21.17933, 22.33067, 22.35667, 22.56867…
$ `Adj Close` <dbl> 20.67467, 20.02400, 21.17933, 22.33067, 22.35667, 22.56867…
$ Volume      <dbl> 174879000, 104478000, 110911500, 113268000, 105127500, 814…

Заповнюємо пропуски

tesla = tesla %>%
  fill_gaps() %>% 
  fill(Open, .direction = "down") %>% 
  fill(High, .direction = "down") %>%
  fill(Low, .direction = "down") %>%
  fill(Close, .direction = "down") %>%
  fill(`Adj Close`, .direction = "down") %>%
  fill(Volume, .direction = "down")

tesla %>% 
  filter(High > 100) %>% 
  autoplot(Volume) +
  labs(title = "Об'єм проданих акцій за день за умови, що у цей день максимальна
       ціна була більше 100 доларів", y = "Штук")

Модель для прогнозування

fit <- tesla %>%
  model(trend_model = TSLM(High ~ trend()))

fit %>% slice(1:3)

# A mable: 1 x 1
  trend_model
      <model>
1      <TSLM>

Прогноз найвищої щоденної ціни акції на 1 рік

forecast_model <- fit %>%
  fabletools::forecast(h = "1 year")

forecast_model %>%
  autoplot()

Білий шум

gg_tsresiduals(fit)

Довірчі інтервали

forecast_model %>% 
  hilo(level = 95)

# A tsibble: 365 x 5 [1D]
# Key:       .model [1]
   .model      Date               High .mean                  `95%`
   <chr>       <date>           <dist> <dbl>                 <hilo>
 1 trend_model 2022-10-29 N(353, 2552)  353. [254.4233, 452.4460]95
 2 trend_model 2022-10-30 N(354, 2552)  354. [254.6968, 452.7202]95
 3 trend_model 2022-10-31 N(354, 2552)  354. [254.9703, 452.9943]95
 4 trend_model 2022-11-01 N(354, 2552)  354. [255.2439, 453.2684]95
 5 trend_model 2022-11-02 N(355, 2552)  355. [255.5174, 453.5426]95
 6 trend_model 2022-11-03 N(355, 2552)  355. [255.7909, 453.8167]95
 7 trend_model 2022-11-04 N(355, 2552)  355. [256.0644, 454.0909]95
 8 trend_model 2022-11-05 N(355, 2552)  355. [256.3380, 454.3650]95
 9 trend_model 2022-11-06 N(356, 2552)  356. [256.6115, 454.6391]95
10 trend_model 2022-11-07 N(356, 2552)  356. [256.8850, 454.9133]95
# … with 355 more rows