Time sequence prediction is a vital process in varied industries, together with finance, healthcare, provide chain administration, and local weather modeling. The flexibility to foretell future tendencies primarily based on previous information supplies a aggressive benefit in decision-making and strategic planning.
Recurrent Neural Networks (RNN) and Lengthy Quick-Time period Reminiscence (LSTM) networks are two of essentially the most generally used architectures for time sequence forecasting. However which one is best? To reply this query, we have to dive deep into their architectures, strengths, weaknesses, and sensible purposes.
