LSTM (Long Short-Term Memory, in neural networks)

Long Short-Term Memory, or LSTM, is a type of artificial neural network used in the field of deep learning, which is a subset of machine learning. These networks are designed to recognize patterns in sequences of data, such as time series data, speech, text, or video. Think of LSTM like a very attentive student who not only takes notes during a lecture but also remembers important points from past lectures to better understand the topic.

LSTMs are particularly well-suited for making predictions based on time series data, which means they are excellent for tasks like forecasting stock prices, predicting the next word in a text, or understanding spoken language. This is because they can remember information for a long period and are able to retain their memory of previous inputs thanks to their internal mechanisms.

In business intelligence and data analytics, LSTMs can provide a powerful way to analyze and make predictions from complex and sequential data. They help organizations uncover deep insights, recognize patterns, and make more accurate predictions about future trends. This ability to learn from the ‘long-term’ context of data makes LSTMs invaluable to businesses that require nuanced understanding and forecasting from their data analytics.