The guide offers a comprehensive overview of selecting models for time series prediction, covering classical methods like ARIMA and SARIMA, supervised machine learning models such as linear regression and random forest, and advanced deep learning models like LSTMs and Prophet. It emphasizes understanding the unique characteristics of time series data, such as autocorrelation, seasonality, and stationarity, and provides insights into model evaluation through metrics like Mean Squared Error and cross-validation techniques. The guide also includes practical examples using Python libraries for model implementation and evaluation, highlighting the importance of feature engineering and choosing the appropriate forecasting horizon. The article concludes with a case study on forecasting S&P 500 stock prices, demonstrating model experimentation and selection using tools like Neptune for result tracking.