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
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
- Kalman smoothing
- Moving average
- Seasonal decomposition
- MICE interpolation
- Optimal local average
Audience level: Intermediate
Date & Time: 6 November 2020, Friday 17:10-17:40
Speaker: Sara Iris Garcia
Sara is a seasoned software engineer and a data science enthusiast. She holds a master’s degree in
Data Science and her main research interest is the application of artificial intelligence in medicine.
When she is not coding, she spends her free time baking sweet treats and watching Rick and
Sara is very active in the Python community. She is the co-lead of PyLadies Guatemala City. She
also efforts in empowering women in tech by leading the Women in Data Guatemala city chapter
and the Papers we Love Guatemala city chapter, where she organizes events focused on increasing
the visibility of women in steam careers.