What is LSTM and how is it used in time series forecasting?

What is LSTM and how is it used in time series forecasting?

LSTM stands for Long Short-Term Memory, a type of recurrent neural network (RNN) that is effective for time series forecasting due to its ability to learn long-term dependencies.

Can you explain how LSTM handles long-term dependencies?

LSTM uses a special architecture with gates that control the flow of information, allowing it to retain information over long periods and forget irrelevant data.

What are some practical applications of LSTM in time series?

LSTM is used in various applications like stock price prediction, weather forecasting, and even in natural language processing tasks.

How does LSTM compare to traditional time series models?

Unlike traditional models, LSTM can capture complex patterns and relationships in data, making it more suitable for non-linear time series.