#105 Machine Learning & Data Science Challenge 105


What is Exponential smoothing?
Exponential smoothing uses similar logic to the moving average, but this time, different decreasing weight is assigned to each observation.
We can also say, less importance is given to the observations as we move further from the present.
Mathematically, exponential smoothing is expressed as:
- Here, alpha is the smoothing factor that takes values between 0 to 1. It determines how fast the weight will decrease for the previous observations.
From the above plot, the dark blue line represents the exponential smoothing of the time series using a smoothing factor of 0.3, and the orange line uses a smoothing factor of 0.05. As we can see, the smaller the smoothing factor, the smoother the time series will be.
Because as the smoothing factor approaches 0, we approach the moving average model.
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Written by

Bhagirath Deshani
Bhagirath Deshani
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.