Basic Time Series Forecasting

Martin Schedlbauer, PhD

Introduction to Forecasting

  • Builds predictive models based on trends in time series data
  • Essential in fields like finance, economics, and science for predicting future values
  • Relies on historical data to identify patterns and trends to predict future outcomes

Lesson Overview

Key concepts covered in this lesson:

  • Fundamental principles of forecasting
  • Various forecasting methods
  • Practical implementations using R

Forecasting Techniques

  • Simple Methods
    • Moving Averages
    • Weighted Moving Averages
  • Statistical Methods
    • ARIMA
    • Exponential Smoothing
    • Linear Regression
  • Advanced Techniques
    • Prophet
    • LSTM Neural Networks

Sample Data

Average daily water use (in liters)

period month year volume days daily
1 Aug 2023 13 32 406
2 Sep 2023 10 30 333
3 Oct 2023 10 33 303
4 Nov 2023 20 30 665

Moving Averages

Summary and Conclusion

  • Recap of Key Points
  • Importance of Accurate Forecasting and Predictive Modeling
  • Final Thoughts

Q&A

3.303L | Forecasting