A simple introduction to Autocorrelation, Moving Average, and in the second part, an introduction to STL Decomposition, Seasonal Trends, and much more!

A simple introduction to Seasonal Trends, Decompositions, and many more! This is the second and last part of the lessons on Autocorrelation, Moving Averages, Trends, and Decompositions. For the code used in this

In this post I will be introducing the math behind Gradient Descent and how it works!

To prepare for the next lesson, I wanted to provide a quick lesson explaining what stationary data is. What is Stationarity By textbook definition, a stationary time series (a dataset which is a

Before we take a deep dive into how ARIMA models work, I wanted to show you some alternatives, mainly using Python ML libraries to do the prediction/forecasting. In this lesson, we will be

Here, we will learn what ARIMA models are, how they work, and how they can be useful; we will also implement an Autoregressive model from SCRATCH!

Here, we will build off the last lesson and properly implement an Autoregressive, Integrated, Moving Average Model. As usual, I will be using the same data as in the previous lesson. The fundamental

In the previous lessons, we have learned topics from Autocorrelation, Moving Average, and Differencing, to ARIMA Models and Multiple Regression. And we have applied these methods to various datasets, to better understand how