How to Analyse Irregular Time-Series in R

In the world of data, time-series data refers to information collected over time. When we talk about "irregular time-series data," we mean data collected at inconsistent or random times, rather than at fixed, regular intervals. This article will explain about irregular time-series data , how to handle it, and how to analyze it effectively.

What is Irregular Time-Series?

An irregular time-series is a collection of data points collected at uneven or random times, not at regular intervals. For example, if you record the temperature whenever you feel like it, rather than every hour or every day, that’s irregular. It’s different from regular time-series data, where you collect data at consistent intervals, like every hour or every day.

• Recording the temperature whenever you remember to check it.
• Logging the number of website visits sporadically throughout the month.
• Noting down the rainfall whenever you see it raining.

Analytical Techniques for Irregular Time-Series

Once your data is ready, you can analyze it using different techniques:

• Resampling: Convert your irregular data to a regular format (e.g., monthly) using resampling techniques. This can make it easier to analyze.
• Plotting: Create plots to visualize the data and see patterns or trends. Use basic plotting functions to see how your data behaves over time.
• Trend Analysis: Calculate moving averages to smooth out the data and identify trends.
• Forecasting: Use statistical models like ARIMA to make predictions about future values.

Let's implement stepwise How to handle Irregular Time-Series in R Programming Language.

Step 1: Install and Load Required Packages

First we will Install and Load Required Packages.

install.packages("zoo")
install.packages("imputeTS")

library(zoo)
library(imputeTS)

Step 2: Create Irregular Time-Series Data

Now we will Create Irregular Time-Series Data.

# Create irregular time-series data
set.seed(123)
dates <- as.Date(c('2024-01-01', '2024-01-03', '2024-01-04', '2024-01-10', '2024-01-11', '2024-01-15'))
values <- c(10, 15, NA, 25, 30, NA)
irregular_ts <- zoo(values, dates)
print("Irregular Time-Series Data:")
print(irregular_ts)

Output:

Irregular Time-Series Data:
2024-01-01 2024-01-03 2024-01-04 2024-01-10 2024-01-11 2024-01-15 
        10         15         NA         25         30         NA 

• First create a time-series object with some missing values.
• Display the original irregular time-series data to verify its structure.

Step 3: Resample to Regular Intervals

Now we will Resample to Regular Intervals.

regular_dates <- seq(start(irregular_ts), end(irregular_ts), by = "day")
regular_ts <- merge(irregular_ts, zoo(, regular_dates))
cat("\nResampled Regular Time-Series Data:\n")
print(regular_ts)

Output:

Resampled Regular Time-Series Data:
2024-01-01 2024-01-02 2024-01-03 2024-01-04 2024-01-05 2024-01-06 2024-01-07 
        10         NA         15         NA         NA         NA         NA 
2024-01-08 2024-01-09 2024-01-10 2024-01-11 2024-01-12 2024-01-13 2024-01-14 
        NA         NA         25         30         NA         NA         NA 
2024-01-15 
        NA 

• Convert the irregular time-series to a regular daily time-series for consistent analysis.
• Creates a sequence of daily dates and merges it with the original time-series to fill in the gaps.
• Display the resampled data to confirm it has been converted to a regular daily series.

Step 4: Interpolate Missing Values

Now we will Interpolate Missing Values.

interpolated_ts <- na_interpolation(regular_ts, option = "linear")
cat("\nInterpolated Time-Series Data:\n")
print(interpolated_ts)

Output:

Interpolated Time-Series Data:
2024-01-01 2024-01-02 2024-01-03 2024-01-04 2024-01-05 2024-01-06 2024-01-07 
  10.00000   12.50000   15.00000   16.42857   17.85714   19.28571   20.71429 
2024-01-08 2024-01-09 2024-01-10 2024-01-11 2024-01-12 2024-01-13 2024-01-14 
  22.14286   23.57143   25.00000   30.00000   30.00000   30.00000   30.00000 
2024-01-15 
  30.00000 

• Fill in the missing values in the resampled time-series using linear interpolation.
• Applies linear interpolation to estimate missing values between existing data points.
• Display the data after interpolation to verify that missing values have been filled.

Step 5: Visualization

At last we will visualize the data.

# Calculate range for y-axis to include all data points
valid_range <- range(coredata(regular_ts), coredata(interpolated_ts), na.rm = TRUE)

# Plot original data with points
plot(index(regular_ts), coredata(regular_ts), type = "p", col = "red", 
     ylim = valid_range, ylab = "Values", xlab = "Date", 
     main = "Handling Irregular Time-Series Data", pch = 16)

# Add interpolated data with lines
lines(index(interpolated_ts), coredata(interpolated_ts), type = "o", col = "blue")

# Add legend to the plot
legend("topright", legend = c("Original", "Interpolated"), col = c("red", "blue"), 
       lty = 1, pch = c(16, NA), lwd = 1)

Output:

• valid_range Calculation: Ensures that the y-axis range includes all data points.
• plot() Function: Plots the original data as points.
• lines() Function: Adds the interpolated data as a line to the existing plot.
• legend() Function: Adds a legend to differentiate between original and interpolated data.
• Now visualize the original and interpolated time-series data to compare them.

Conclusion

Working with irregular time-series data involves modifying the data to address issues like varying frequencies and missing values. In R, this can be done through resampling, interpolation, and specialized packages. These techniques help manage irregular intervals, improving the accuracy of forecasts and analyses. Effectively handling irregular time-series data enhances the reliability of statistical models and supports better decision-making across various fields.

surajbumrgc

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Article Tags :

• R Language
• AI-ML-DS With R
• Time Series