Time series are sets of data that are collected over time, at regular or irregular intervals. These data are collected and recorded in sequence, often over years, months, weeks, days, or hours, and are used to analyze and predict trends or patterns over time.
Time series are used in many areas of study, including economics, meteorology, engineering, environmental science, physics, medicine, and market research, among others. Some examples of time series include data on temperature, rainfall, traffic, stock prices, industrial production, website visitor numbers, consumer behavior, and many other variables that change over time.
Steps to create a time series in R:
- Collect the data: Collect the data that you want to analyze. Make sure that the data is in an appropriate format, such as a CSV file or an R data frame.
- To perform time series analysis in R, you need to load the following libraries:
ts: This library provides functions for creating and manipulating time series objects in R.
forecast: This library provides a variety of functions and techniques for modeling and forecasting time series.
ggplot2: This library provides functions for creating high-quality statistical graphics in R.
lubridate: This library provides functions for working with dates and times in R.
You can load the libraries in R using the library() function. For example:
library(tseries)
library(forecast)
library(lubridate)
library(ggplot2)
Note that if you do not have any of these libraries installed, you must install them before using them, using the command install.packages("package_name").
- Make sure to specify the correct path and filename for the CSV file when loading the data with
read.csv()function.
setwd("D:/Documents/serie_temporal")
data <- read.csv("wf_Jug_neo_ts.csv")
4. Set the “start” and “end” variables with dates, which will be used in the next step.
inicio <- as.Date("2001-01-01")
final <- as.Date("2021-12-31")
5. Use the ts() function to convert the data into a time series, using the previously defined “start” and “end” variables, and a specific frequency.
data_ts <- ts(data$wf_count, start=c(2001,01), end = c(2021,12),
frequency = 12)
6. Use the plot() function to plot the time series data.
plot(data_ts)
7. Finally, export the plot.
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