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Time Series Analysis and Forecasting with GPT-4o

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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.


01 - Introduction
  • 01 - Hire GPT-4o as your forecaster
  • 02 - Human history of forecasting

  • 02 - 1. Fundamentals of Time Series Forecasting
  • 01 - Essentials of time series forecasting
  • 02 - Trends, seasonality, cycles, and noise components in time-series data
  • 03 - Additive decomposition, multiplicative decomposition, and STL decomposition

  • 03 - 2. Time Series Analysis Techniques
  • 01 - Use autocorrelation and partial autocorrelation plots for time series analysis
  • 02 - Access stationarity vs. non-stationarity by ADF test
  • 03 - Transform non-stationarity to stationarity using differencing

  • 04 - 3. Predictive Modeling and Presentation
  • 01 - Time-series forecasting with ARIMA
  • 02 - Hyperparameter selection for ARIMA
  • 03 - Predicting with exponential smoothing model
  • 04 - How to select the best ETS model
  • 05 - Creating effective PowerPoint presentations

  • 05 - Conclusion
  • 01 - Predict tomorrow to prepare for the future
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    شناسه: 42192
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    مدت زمان: 39 دقیقه
    تاریخ انتشار: ۱۵ اردیبهشت ۱۴۰۴
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