Time Series Forecasting - ARIMA Models and Regression Analysis

Learn to predict future trends using AutoRegressive Integrated Moving Average (ARIMA) in this comprehensive video tutorial. Dive deep into the foundations of time series forecasting, starting with a thorough understanding of regressions using AutoReg. Master essential skills such as working with DateTimes, handling frequency, dropping columns, cleaning missing data, and selecting data based on conditions. Explore the power of ARIMA, a fundamental model that forms the basis for many advanced forecasting techniques, and discover how it examines differences between values to project future outcomes. Gain practical experience by applying these concepts to real-world datasets, including stock market and birth rate data. Develop the ability to create models, make predictions, and interpret results using various statistical tools and tests.

Intro
ARIMA
Regression
Getting Data
Setting Frequency
Plotting
Creating the model
Making predictions
Apple Data
Plot Apple Data
Integrated Integration
Masking
US Birth Rates
Different Data
ADF Test
Nonstationary