Missing Data: Time Series & Diagnostics
Missing Data: Time Series & Diagnostics groups the core ideas a learner needs at the 4 · data cleaning & missing data stage. Work through the lessons in order when new to the area, or use them independently as a reference when implementing an analysis.
Learn the mechanism one decision at a time
Work through the lessons in order if the topic is new. If you already know the basics, open the specific leaf lesson that matches the operation, diagnostic or failure mode you need.
1Definition→
2Mechanism→
3Example→
4Diagnostic→
5Decision
Forward fillForward fill is a practical concept within Missing Data: Time Series & Diagnostics. It helps turn the broader workflow stage “4 · Data Cleaning & Missing Data” into an explicit analytical decision that can be explained, implemented and checked. The concept should be understood in terms of purpose, mechanism, assumptions, evidence and downstream consequences.
02Backward fillBackward fill is a practical concept within Missing Data: Time Series & Diagnostics. It helps turn the broader workflow stage “4 · Data Cleaning & Missing Data” into an explicit analytical decision that can be explained, implemented and checked. The concept should be understood in terms of purpose, mechanism, assumptions, evidence and downstream consequences.
03Linear interpolationLinear interpolation estimates a missing value between two observed points by assuming a straight-line change across the gap. It is common in ordered or time-indexed numeric data when short gaps are plausible.
04Time-aware interpolationTime-aware interpolation is a practical concept within Missing Data: Time Series & Diagnostics. It helps turn the broader workflow stage “4 · Data Cleaning & Missing Data” into an explicit analytical decision that can be explained, implemented and checked. The concept should be understood in terms of purpose, mechanism, assumptions, evidence and downstream consequences.
05Interpolation limits and gapsInterpolation limits and gaps is a practical concept within Missing Data: Time Series & Diagnostics. It helps turn the broader workflow stage “4 · Data Cleaning & Missing Data” into an explicit analytical decision that can be explained, implemented and checked. The concept should be understood in terms of purpose, mechanism, assumptions, evidence and downstream consequences.
06Validate imputation distributionsValidate imputation distributions is a practical concept within Missing Data: Time Series & Diagnostics. It helps turn the broader workflow stage “4 · Data Cleaning & Missing Data” into an explicit analytical decision that can be explained, implemented and checked. The concept should be understood in terms of purpose, mechanism, assumptions, evidence and downstream consequences.
07Compare model performance across imputation strategiesCompare model performance across imputation strategies represents a family or practice in model building. The central idea is to define what structure can be learned, how model quality is measured during fitting, and how generalisation is tested on observations not used to choose the model.