The document discusses time series analysis and forecasting, emphasizing the correlation of variables over time and the importance of stationarity. It includes examples from the banking industry and showcases hands-on demonstrations of various modeling techniques such as time series decomposition and prediction modeling. Key algorithms mentioned are Holt-Winters and moving average for forecasting.

1. Time Series Analysis/ Forecasting
Samudra Kanankearachchi
Senior Software Architect @99XTechnology
Data Science Specialist

3. State of variable is
correlated to it self
t
t - 1
t + 1

4. Common time series patterns

5. Example Use Case (Banking Industry)

7. 1

8. 2

11. Stationarity / Differencing (None Stationary)
Importance of managing time series
Poor Indicators
Uncontrolled Positive or Negative Trends
Unhealthy Variation
Positive /Negative long runs away from the
mean
…

12. Hands on Demos
Modeling / Training
and Forecasting
Demonstrations

13. Demo

14. Time series Decomposition (Demo)
Demo

15. Time series Prediction Modeling (Demo)
Time Series Data Model
HoltWinters (Algorithm)
Perdition Model )
Perditions
Demo

16. Moving Average Base forecasting (Demo)
Demo

17. Thank You