Introduction

Time series data is a fundamental concept in data analysis, and merging irregular time series with regular ones can be challenging. In this article, we will explore how to add data from an irregular time series to a timeseries with 5-minute timesteps.

Background

In the context of time series data, a regular time series refers to a dataset where each observation is associated with a fixed interval of time. For example, a temperature sensor that measures temperature every five minutes would produce a regular time series. On the other hand, an irregular time series contains observations at variable intervals, making it difficult to align with a regular schedule.

Solution

To solve this problem, we will use R programming language and its built-in data structures data.frame and sapply.

Step 1: Load Required Libraries

# Load required libraries
library(dplyr)
library(lubridate)

In the above code:

• The dplyr library is used for data manipulation, including filtering and joining datasets.
• The lubridate library provides a set of classes for working with dates and times in R.

Step 2: Define Helper Function

# Define helper function to find the nearest index position
findNearest <- function(x, y) {
    which.min(abs(x - y))
}

In this code:

• The findNearest function takes two arguments: a vector of dates (x) and the corresponding date in the irregular time series (y).
• It returns the index position of the nearest match between the regular and irregular time series.

Step 3: Create Index Vector

# Create an index vector using sapply with findNearest function
idx <- sapply(activity$Date, findNearest, temperature$Date)

In this code:

• The sapply function applies the findNearest function to each date in the regular time series (activity$Date) and returns an index vector (idx) that specifies which temperature values correspond to each date.

Step 4: Assign Temperature Values

# Assign temperature values from irregular time series to regular time series
activity$temp <- temperature$temp[idx]

In this code:

• The temp column of the regular time series is assigned the corresponding temperature value from the irregular time series using the index vector created in Step 3.

Example Output

The resulting dataset after merging the regular and irregular time series can be seen below:

# Print the merged dataset
head(activity)
                    Date Activity   temp
1220 2012-10-18 06:36:59      300 12.625
1221 2012-10-18 06:41:59      300 12.625
1222 2012-10-18 06:46:59      300 12.625
1223 2012-10-18 06:51:59      300 12.625
1224 2012-10-18 06:56:59      300 12.625
1225 2012-10-18 07:01:59      300 12.625

tail(activity)
                    Date Activity temp
1233 2012-10-18 07:41:59      300 12.5
1234 2012-10-18 07:46:59      300 12.5
1235 2012-10-18 07:51:59      300 12.5
1236 2012-10-18 07:56:59      300 12.5
1237 2012-10-18 08:01:59      300 12.5
1238 2012-10-18 08:06:59      300 12.5

Conclusion

By using the sapply function and a helper function to find the nearest index position, we can efficiently merge an irregular time series with a regular one. This approach provides accurate results while preserving the original data structure.

In this article, we discussed how to add data from an irregular time series to a timeseries with 5-minute timesteps. We covered the steps involved in solving this problem using R programming language and its built-in data structures data.frame and sapply. Additionally, we provided example code snippets to illustrate each step of the process.

In practice, this approach can be applied to various scenarios involving time series data integration and analysis.

Last modified on 2025-01-27