Time Series Analysis for Beginner Data Scientists - Time Series Forecasting

Dive into the world of time series analysis with this comprehensive webinar designed for beginner data scientists with basic coding experience. Learn to navigate, analyze, and model time series data for applications in finance, retail, human resources, and environmental analysis. Master the basics of time series analysis, including terminology, machine learning techniques for model creation, forecasting methods, and validation processes. Explore key concepts such as cross-sectional vs. time series data, time series components, decomposition models, autoregression, moving averages, and the ARIMA model. Gain practical skills in creating interactive dashboards and participate in an exercise using financial stock price data. By the end of this 69-minute session, you'll be equipped to tackle time series problems and apply your newfound knowledge to real-world scenarios.

Intro
Cross Sectional VS. Time Series
Why is Time Series Important
Creating Your Time Series Problem
Time Series Components
Decomposition Model
Autoregression
Moving Average
Stationarity and Augmented Dickey-Fuller Test
Integration - ARIMA Model
Residual Analysis
Ljung-Box Test
Aditional Questions
Autocorrelation Function
Interpretating ACF and PACF Plots
Interpreting Seasonal Orders
Conclusion
Q&A