ARIMA, SARIMA, and Their Relationship with Deep Learning for Time Series Forecasting A Deep Dive into ARIMA, SARIMA, and Their Relationship with Deep Learning for Time Series Forecasting In recent years, deep learning has become a dominant force in many areas of data analysis, and time series forecasting is no exception. Traditional models like ARIMA (Autoregressive Integrated Moving Average) and its seasonal extension SARIMA have long been the go-to solutions for forecasting time-dependent data. However, newer models based on Recurrent Neural Networks (RNNs), particularly Long Short-Term Memory (LSTM) networks, have emerged as powerful alternatives. Both approaches have their strengths and applications, and understanding their relationship helps in choosing the right tool for the right problem. In this blog post, we’ll explore ARIMA and SARIMA models in detail, discuss how they compare to deep learning-based models like RNNs, and demonstrate their practical implementation. Deep Learning and Time Series Forecasting Deep learning is a subset of machine learning where models learn hierarchical features from data using multiple layers of neural networks. When it comes to time series forecasting, one of the most common deep learning architectures used is Recurrent Neural Networks (RNNs). RNNs are particularly well-suited for time series because they are…
