#93 Machine Learning & Data Science Challenge 93
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.
Why you cannot take non-stationary data to solve the time series Problem?
o Most models assume stationary data. In other words, standard techniques are invalid if data is "NON-STATIONARY".
o Autocorrelation may result due to "NON-STATIONARY".
o Non-stationary processes are a random walk with or without a drift (a slow, steady change).
o Deterministic trends (trends that are constant, positive or negative, independent of time for the whole life of the series).
