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Forecast Time Series Data using Deep Learning and Long Short Term Memory Networks

In this project, we are going to understand how to apply the deep learning paradigm to forecast univariate time series data.

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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 model
  • How to develop LSTM model
  • How to evaluate an LSTM recurrent neural network for time series forecasting.

What will you get

  • Access to recording of the complete project
  • Access to all material related to project like data files, solution files etc.


  • Jupyter Notebook from Anaconda installation
  • R and R-Studio installation
  • At least 5MBS internet speed
  • At least 4 GB RAM Machine

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 hackerday is about Long Short Term Memory Network (LSTM) to perform time series forecasting for univariate time series data.