The blog post explores three popular approaches for time series prediction: ARIMA, Prophet, and LSTM, each with distinct methodologies and applications. ARIMA is a mathematical model that leverages past values and errors for predictions, requiring the time series to be stationary. Prophet, developed by Facebook, is tailored for business time series with components for trend, seasonality, holidays, and random fluctuations. LSTM, a recurrent neural network, processes sequences of variable lengths and is adaptable beyond time series data. The post evaluates these models using stock data from Bajaj Finserv Ltd, revealing that ARIMA performs best in terms of mean square error and mean absolute error, while LSTM shows potential overfitting issues due to its complexity. The blog highlights the importance of hyperparameter tuning and the specific advantages and limitations of each model, emphasizing the need for careful model selection based on data characteristics and prediction goals.