A Comparison Between Long Short-Term Memory And Prophet For Time Series Analysis And Forecasting Technique

Abstract

Time series analysis and forecasting are vital to many industries, such as resource management, economics, and weather forecasting. The ability of deep learning approaches, especially Networks employing Long Short-Term Memory (LSTM), to capture intricate temporal correlations has made them more and more popular recently. Furthermore, because of their ease of use and interpretability, conventional statistical techniques like Prophet have gained widespread traction. In this study, the prediction and time series analysis abilities of Prophet and LSTM are examined. We assess the effectiveness of these methods using empirical data while taking precision, computing economy, and usability into consideration.

Our findings suggest that while LSTM networks excel in capturing intricate patterns and long-term dependencies, they require substantial data preprocessing and tuning.

Prophet is better suited for scenarios with large periodic components that require rapid prototyping and reporting, since it provides a more straightforward modeling approach that accounts for seasonality and holiday support.

The specifics of the forecasting problem and the resources available for model construction and training ultimately determine which of LSTM and Prophet is best.