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# Load necessary libraries
library(tidyverse)   # Data manipulation and visualization
## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr     1.1.2     ✔ readr     2.1.4
## ✔ forcats   1.0.0     ✔ stringr   1.5.0
## ✔ ggplot2   3.4.2     ✔ tibble    3.2.1
## ✔ lubridate 1.9.3     ✔ tidyr     1.3.0
## ✔ purrr     1.0.1     
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
library(lubridate)   # Date manipulation
library(forecast)    # Time series forecasting
## Registered S3 method overwritten by 'quantmod':
##   method            from
##   as.zoo.data.frame zoo
library(ggplot2)     # Data visualization
library(zoo)         # Time series objects
## 
## Attaching package: 'zoo'
## 
## The following objects are masked from 'package:base':
## 
##     as.Date, as.Date.numeric

Load the dataset (assuming it’s in CSV format)

bike_data <- read.csv(“bike_rental_data.csv”)

Check the first few rows of the dataset

head(bike_data)

Convert date column to Date type if needed

bike_data\(date <- as.Date(bike_data\)date)

Handling missing values (if any)

bike_data <- na.omit(bike_data)

Check the structure of the dataset

str(bike_data)

Convert dataset into a time series object

ts_bikes <- ts(bike_data$rentals, start = c(2011, 1), frequency = 365)

Decompose the time series into components

decomposed_ts <- decompose(ts_bikes)

Plot decomposed time series

plot(decomposed_ts)

Convert dataset into a time series object

ts_bikes <- ts(bike_data$rentals, start = c(2011, 1), frequency = 365)

Decompose the time series into components

decomposed_ts <- decompose(ts_bikes)

Plot decomposed time series

plot(decomposed_ts)

Fit ARIMA model

arima_model <- auto.arima(ts_bikes)

Check the model summary

summary(arima_model)

Forecast the next 12 months

forecasted_values <- forecast(arima_model, h = 12)

Plot the forecasted values

autoplot(forecasted_values) + labs(title = “Forecast of Bike Rentals for the Next Year”, x = “Time”, y = “Forecasted Rentals”)