How do you apply machine learning to your time series? What can you accomplish by doing so? How do you do this? These questions and many more will get answered in this session led by our Machine Learning Developer Advocate, Anais Dotis-Georgiou.

1. Anais Dotis-Georgiou
Machine Learning and
Time Series Data

2. Welcome

3. Agenda
● Intro to Machine Learning
● K-Means
○ What is K-Means Clustering?
○ How is it used for time series data?
○ Code showing how K-Means is used
● https://github.com/Anaisdg/K-Mean
s_Influx
● https://github.com/mrahtz/sanger-m
achine-learning-workshop/blob/mas
ter/Unsupervised%20Learning.ipyn
b

4. K-Means: How does it apply to time series data?
● Step back
● Why should we use machine learning for time series data at all?

5. Moving Average

6. Stdev

7. Quartile Range

10. K-Means: What is K-Means Clustering
● 4 major categories: unsupervised, semi-supervised, supervised, and
reinforcement learning
● K-Means is an unsupervised learning technique
● Supervised vs Unsupervised
● K-Nearest Neighbor

15. ● Euclidean:
● Centroid’s position:
●

16. Some code showing how I used K-Means
● Time series data is clusterable?
● Steps anomaly detection:
○ Segmentation & Windowing
○ Clustering
○ Reconstruction
○ Alerting

17. Segmentation
Take your data that looks like this...

18. Turn it into this...

19. Clustering
● Segment size
determines
polynomial
shape
● Cluster number
can determine
how much
coefficients
matter

20. Resources

21. K-Means Links
● https://github.com/Anaisdg/K-Means_Influx
● https://www.influxdata.com/blog/why-use-k-means-for-time-series-data-part-o
ne/
● https://medium.com/@dganais/why-use-k-means-for-time-series-data-part-tw
o-690e771c0b36
● https://github.com/mrahtz/sanger-machine-learning-workshop

22. Holt-Winters Links
● https://grisha.org/blog/2016/01/29/triple-exponential-smoothing-forecasting/
● https://docs.influxdata.com/influxdb/v1.6/query_language/functions/#holt-wint
ers
●

23. LoudML Links
● https://www.influxdata.com/blog/applying-machine-learning-models-to-influxd
b-with-loud-ml-docker-for-time-series-predictions/
● https://loudml.io/

24. Thank You