Baselines

Explore baseline forecasting techniques in time series analysis. Learn to calculate simple average and naive forecasts using Python and understand their role as benchmarks to assess model accuracy. This lesson helps you compare advanced models against basic baselines to ensure meaningful forecasting results.

We'll cover the following...

Overview
- Simple average
- Naive forecast
How to calculate baselines

Overview

Baselines are important so that we can have something to compare our models to. Imagine we put a lot of work into creating an LSTMLong Short-Term Memory neural network model to forecast sales, and the model doesn't perform better than just using a simple average as the forecast.

So, to evaluate our model's performance, we compare it to a benchmark, which is our baseline. If our model does worse than this, it's probably not a good model.

Simple average

As the name suggests, simple average means just forecasting the mean value for every point in time. If our data has no trend, this might be a good baseline. The formula to calculate the mean is:

1.Introduction to Time Series

2.Python Basics for Time Series

3.Time Series Analysis

4.Basic Time Series Forecasting

Project

5.Advanced Time Series Forecasting

6.Forecast Evaluation

Assessment

7.Practical Examples

Mini Project

8.Conclusion

Mock Interview

Baselines

Overview

Simple average