As a data analyst, you’re no stranger to dealing with missing values in your datasets. But what happens when you’re working with multivariate time series data, and those missing values are not randomly scattered, but instead occur in blocks? This is exactly the challenge I faced recently, and I’m excited to share my journey with you.
The data consisted of multiple subjects, each with multiple variables, and similar missingness patterns. My goal was to identify different states or clusters in the data. Initially, I thought of using PCA and cluster analysis, but the missingness problem seemed to be a major roadblock.
One of the main concerns was that the clusters might be imbalanced, with some states being relatively rare. I was determined to find a way to work directly with the data as is, without imputing the missing values. After some research, I discovered a few methods that could help me achieve my goal.
## Handling Missing Values
One approach is to use sampling and weighting to account for the missingness patterns. This can be done by sampling the data in a way that ensures the missing values are representative of the overall dataset. Weighting can then be applied to give more importance to the samples with fewer missing values.
Another approach is to use algorithms that are specifically designed to handle missing values. For example, the k-prototypes algorithm is a variation of k-means that can handle mixed-type data, including missing values.
## Cluster Analysis Methods
Once the missing value problem is addressed, it’s time to choose a suitable cluster analysis method. Since the clusters are likely to be imbalanced, I opted for methods that can handle this issue. One such method is the DBSCAN algorithm, which is robust to noise and can identify clusters of varying densities.
Another method is the k-medoids algorithm, which is similar to k-means but uses medoids instead of centroids. This makes it more robust to outliers and noisy data.
## Working in R
As I work in R, I was excited to find that there are several packages available that can help with cluster analysis and handling missing values. The `flexclust` package provides an implementation of the k-prototypes algorithm, while the `fpc` package offers a range of cluster analysis methods, including DBSCAN and k-medoids.
## Conclusion
Working with multivariate time series data with missing values can be challenging, but it’s not impossible. By using the right methods and techniques, you can uncover hidden patterns and identify clusters in the data. Remember to always consider the missingness patterns and handle them appropriately, and don’t be afraid to experiment with different cluster analysis methods until you find the one that works best for your data.
**Further reading:** [Handling missing values in time series data](https://www.rdocumentation.org/packages/forecast/versions/8.13/topics/na.interp)