{"id":47374,"date":"2021-03-10T06:14:30","date_gmt":"2021-03-10T06:14:30","guid":{"rendered":"https:\/\/www.bridge-global.com\/blog\/blog\/?p=47374"},"modified":"2021-03-10T06:17:14","modified_gmt":"2021-03-10T06:17:14","slug":"how-to-develop-predictions-using-lstm","status":"publish","type":"post","link":"https:\/\/www.bridge-global.com\/blog\/how-to-develop-predictions-using-lstm\/","title":{"rendered":"A Simple Way to Perform Prediction on Continuous Data Using LSTM"},"content":{"rendered":"

Long Short-Term Memory networks - usually just called \"LSTM\" - are a special type of Recurrent Neural Network or RNN, which can learn long-term dependencies.<\/p>

They were introduced by Hochreiter & Schmidhuber and were refined and made popular by many in their work. They work incredibly well on a variety of problems and are now widely used. LSTM is specifically designed to avoid dependency problems. Remembering information for a long time is almost their default behavior, not something they have a hard time learning!<\/p>

All RNNs have the form of a chain of repeating neural network units. In standard RNNs, this repeating unit will have a very simple structure, such as a tanh layer.<\/p><\/div><\/div><\/div><\/div><\/div><\/div><\/div><\/div>

 A step-by-step guide<\/h2>\n

To import the continuous data, first we need to train the model. Here in the example, I have shown an empty list in which you can populate your required data.<\/span><\/p><\/div><\/div><\/div><\/div><\/div><\/div><\/div><\/div>

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\"Develop<\/div>