Главная
Study mode:
on
1
Intro
2
Cross Sectional VS. Time Series
3
Why is Time Series Important
4
Creating Your Time Series Problem
5
Time Series Components
6
Decomposition Model
7
Autoregression
8
Moving Average
9
Stationarity and Augmented Dickey-Fuller Test
10
Integration - ARIMA Model
11
Residual Analysis
12
Ljung-Box Test
13
Aditional Questions
14
Autocorrelation Function
15
Interpretating ACF and PACF Plots
16
Interpreting Seasonal Orders
17
Conclusion
18
Q&A
Description:
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.

Time Series Analysis for Beginner Data Scientists - Time Series Forecasting

Data Science Dojo
Add to list
0:00 / 0:00