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Time Series Forecasting with LSTM Neural Network Python

Deep Learning Project- Learn to apply deep learning paradigm to forecast univariate time series data.
What are the prerequisites for this project?
  • Jupyter Notebook from Anaconda installation
  • R and R-Studio installation
  • At least 5MBS internet speed
  • At least 4 GB RAM Machine

What will you learn

  • How to develop a baseline of performance for a forecast problem.
  • How to design a robust test harness for one-step time series forecasting.
  • How to prepare data for LSTM recurrent neural network python model
  • How to develop LSTM python model
  • How to evaluate an LSTM recurrent neural network for time series forecasting.

Project Description

Deep learning is an upcoming field, where we are seeing a lot of implementations in the day to day business operations, including segmentation, clustering, forecasting, prediction or recommendation etc. Deep learning architecture has many branches and one of them is the recurrent neural network (RNN), the method that we are going to analyze in this deep learning project is about Long Short Term Memory Network (LSTM) to perform time series forecasting for univariate time series data.



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