Time Series Analysis and Forecasting with GPT-4o

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Alina Zhang

38:37

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01 - Introduction

01 - Hire GPT-4o as your forecaster.mp4

00:45

02 - Human history of forecasting.mp4

01:29

02 - 1. Fundamentals of Time Series Forecasting

01 - Essentials of time series forecasting.mp4

02:35

02 - Trends, seasonality, cycles, and noise components in time-series data.mp4

03:59

03 - Additive decomposition, multiplicative decomposition, and STL decomposition.mp4

04:31

03 - 2. Time Series Analysis Techniques

01 - Use autocorrelation and partial autocorrelation plots for time series analysis.mp4

03:25

02 - Access stationarity vs. non-stationarity by ADF test.mp4

03:27

03 - Transform non-stationarity to stationarity using differencing.mp4

03:30

04 - 3. Predictive Modeling and Presentation

01 - Time-series forecasting with ARIMA.mp4

03:01

02 - Hyperparameter selection for ARIMA.mp4

02:27

03 - Predicting with exponential smoothing model.mp4

02:59

04 - How to select the best ETS model.mp4

02:18

05 - Creating effective PowerPoint presentations.mp4

03:32

05 - Conclusion

01 - Predict tomorrow to prepare for the future.mp4

00:39

Description

One of the highlights of this course is that no coding is required. Now, you can communicate with GPT-4o using human language to analyze, forecast, and visualize time series data. Start with the essentials of what can be forecasted and dive deep into the components of time series data like trends, seasonality, cycles, and noise. Learn to decompose data using additive, multiplicative, and STL methods.

Discover how to analyze time series with autocorrelation and partial autocorrelation plots, assess stationarity using the ADF test, and transform non-stationary data. Advance to predictive modeling with ARIMA and exponential smoothing models, mastering hyperparameter tuning and model selection. By the end of this course, you’ll be equipped to predict tomorrow and prepare for the future with confidence.

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Artificial Intelligence

Alina Zhang

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Linkedin Learning

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LinkedIn Learning is an American online learning provider. It provides video courses taught by industry experts in software, creative, and business skills. It is a subsidiary of LinkedIn. All the courses on LinkedIn fall into four categories: Business, Creative, Technology and Certifications. It was founded in 1995 by Lynda Weinman as Lynda.com before being acquired by LinkedIn in 2015. Microsoft acquired LinkedIn in December 2016.