Time series data without missing values or gaps are a general prerequisite in performing analyses. but what can we do when our data contains gaps and what techniques can we use to fill these values?
Let us revise some of the widest used gap-filling techniques. Some of the techniques I will cover in this talk are:
- Linear interpolation
- Spline interpolation
- Simple Moving Average
- Cumulative Moving Average
- Exponential Moving Average
- Kalman Smoothing