Creating a Time Series Object
- Before we start plotting time series data, we need to convert our data into a time series object. This allows us to work with time-based data and perform various time series analyses.
- We can use functions like ts() and xts() to create time series objects in R Programming Language. The ts() function is part of the base R package, while the xts() function is available in the xts package.
- These functions require specifying the data vector, start date, and frequency of observations to create the time series object.
# Load required packages
library(xts)
library(lubridate)
# Sample data
data <- c(10, 20, 15, 25, 30)
dates <- as.Date(c("2024-01-01", "2024-01-02", "2024-01-03", "2024-01-04",
"2024-01-05"))
# Create time series object using ts()
ts_data <- ts(data, start = c(year(dates[1]), month(dates[1])), frequency = 12)
# Create time series object using xts()
xts_data <- xts(data, order.by = dates)
xts_data
Output:
[,1]
2024-01-01 10
2024-01-02 20
2024-01-03 15
2024-01-04 25
2024-01-05 30
Visualizing Seasonality
- Once we have our time series object, we can visualize seasonality in various ways, such as by year, month, or other relevant time units.
- Visualizing seasonality helps us understand recurring patterns in our data and identify any trends or irregularities.
Typical Patterns Indicating Seasonality
- Regular Fluctuations: Seasonal patterns often exhibit regular fluctuations over a specific period, such as daily, weekly, monthly, or yearly.
- Consistent Peaks and Troughs: Data may consistently peak or trough during certain times of the year, indicating seasonal highs and lows.
- Repeated Patterns: Seasonality can manifest as repeated patterns across multiple cycles, showing similar shapes and amplitudes.
- Predictable Variations: Variations occur predictably within a seasonal cycle, with similar magnitudes and timings each year.
Seasonal Plots in R
A seasonal plot is a graphical representation used to visualize and analyze seasonal patterns in time series data. It helps identify recurring patterns, such as monthly or quarterly fluctuations, within a dataset.
For example, let’s consider a retail store that sells winter clothing. The store collects sales data for sweaters over the past few years. By creating a seasonal plot of sweater sales data, the store can observe if there is a consistent increase in sales during the colder months (fall and winter) compared to the warmer months (spring and summer). This visualization can help the store better understand the seasonal demand for sweaters and plan their inventory accordingly.