Time Series Forecasting with LSTM Neural Network Python

Deep Learning Project- Learn to apply deep learning paradigm to forecast univariate time series data.

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What will you learn

  • Understanding the problem statement

  • Importing the problem statement

  • Installing Keras and LSTM

  • Importing the necessary libraries for applying Neural Networks

  • Performing basic EDA and checking for the null values

  • Imputing the null values using appropriate method

  • Plotting a Time Series plot

  • Creating a Dataset matrix for applying LSTM

  • Sequentially initializing a Neural Networks

  • Defining the error function

  • Understanding solver used "Adam"

  • Applying LSTM as training model

  • Visualizing the loss and accuracy with each epoch

  • Tuning the final model and using it to make predictions

  • Saving the predictions made in CSV format

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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Curriculum For This Mini Project

 
  Deep Learning Architectures
06m
  DNN - Deep Neural Network
00m
  CNN - Convolutional Neural Network
01m
  RNN - Recurrent Neural Network
02m
  Deep Belief & Boltzman Network
01m
  Deep Neural Network - Graphical Representation
20m
  Activation Functions
02m
  Perceptron and Bias
02m
  Convolutional Neural Network - Graphical Representation
08m
  Recurrent Neural Network - Graphical Representation
07m
  Deep Belief & Boltzman Network - Graphical Representation
00m
  Problem Statement
01m
  Data Set
05m
  Setting up Libraries
15m
  Setting Theano as backend
01m
  Import Libraries
02m
  Create Seed function
01m
  Normalize the dataset
04m
  Split dataset into training and testing
06m
  Create Dataset Matrix
07m
  Reshape Dataset
05m
  Create RNN or LSTM Model
04m
  Make Predictions
08m
  Calculate Mean Squared Error
03m
  Conclusion
05m