#104 Machine Learning & Data Science Challenge 104
Greetings.
I am a machine learning engineer based in India, possessing a sustained interest in machine learning since my undergraduate studies. I have completed Stanford University's machine learning course (Andrew Ng) via Coursera, and IBM's machine learning and deep learning curriculum. My current focus is on machine learning and data science projects, aiming to leverage my expertise for impactful, real-world problem-solving.
What is the Moving Average?
The moving average model is probably the most naive approach to time series modeling.
This model states that the next observation is the mean of all past observations. Although simple, this model might be surprisingly good, and it represents a good starting point.
Otherwise, the moving average can be used to identify interesting trends in the data.
We can define a window to apply the moving average model to smooth the time series and highlight different trends.
In the plot above, we applied the moving average model to a 24h window.
The green line smoothed the time series, and we can see that there are two peaks in the 24h period.
The longer the window, the smoother the trend will be.
Below is an example of a moving average on a smaller window.
