Handling missing values in time series data
Missing values are a common challenge in time series data and can arise due to various reasons, such as sensor failures, data transmission issues, or simply the absence of recorded observations. As we’ve discussed, two main scenarios often arise:
- Some null values in features: Imagine a stock market analysis where daily trading data is collected. While all trading days are accounted for, the volume of shares traded on certain days may be missing due to reporting errors. This scenario presents a challenge: how do you maintain the integrity of the dataset while ensuring that analyses remain robust?
- Complete rows are missing: Conversely, consider a weather monitoring system that records daily temperatures. If entire days of data are missing – perhaps due to sensor failures – this poses a significant issue. Missing timestamps means you cannot simply fill in values; the absence of data for those days disrupts the...
