Deep Learning - develop a Long Short Term Memory (LSTM) network for time series analysis using Python
Long Short Term Memory (LSTM) network for time series analysis using Python In this document, students can enhance their skills in time series analysis by building a Long Short Term Memory (LSTM) network using Python. LSTM is a specific type of Recurrent Neural Network (RNN) known for its ability to accumulate propagation state throughout the input sequence. This means that the model's output from one timestep becomes the input for the subsequent timestep, allowing the model to make predictions based on both current and prior input, resulting in more reliable prediction outcomes. The persistent accumulation of propagation state is the primary advantage of LSTM models. The specific tasks include: 1. define a many-to-many type sequence prediction problem, such as power consumption forecast for next 3 days 2. Design and develop an LSTM neural network with multiple input time steps and multiple output time steps 3. Design an evaluation metric 4. Conduct performance analysis of the LS
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