Time Series Analysis and Forecasting with ChatGPT

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.

Learning objectives
Perform decomposition to identify and analyze trends, seasonality, cycles, and noise in time series data.
Utilize autocorrelation and partial autocorrelation plots for comprehensive time series analysis.
Assess the stationarity of data using the Augmented Dickey-Fuller (ADF) test.
Transform non-stationary data to stationary using differencing techniques.
Forecast time series data using ARIMA and ETS models.