Learn at your own pace with over 43 lectures and more than 8.5 hours of content.
At the end of this course you will be able to:
- Approximate simple benchmarking methods.
- Recognize time series patterns with moving averages and exponential smoothing methods.
- Choose best methods comparing several forecasting errors’ metrics.
- Appraise if time series is first order stationary or constant in its mean.
- Estimate conditional mean with autoregressive integrated moving average (ARIMA) models.
- Define models’ parameters and evaluate if forecasting errors are random.
- Select best models comparing forecasting errors’ information criteria.
- Check models’ forecasting accuracy comparing their predicting capabilities.
This course is best for you as:
- Student at any knowledge level who wants to learn the subject.
- Academic researcher who wishes to deepen your knowledge.
- Business or financial analysts and data scientist who desires to apply these concepts.
The requirements for this course are:
- Spreadsheet software such as Microsoft Excel®.
- Familiarity with software is recommended.
Feel free to take a look at course curriculum here!