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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Project Template Outcomes

  • 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

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Project Description

Introduction to LSTM Time Series Forecasting in Python

Deep learning is an upcoming field where we see a lot of implementations in the day-to-day business operations, including segmentation, clustering, forecasting, prediction or recommendation, etc. These exciting implementations are realized because of the variety of deep learning architectures that scientists and researchers have developed. One of such models used for time series forecasting is the LSTM model, and it is a particular type of neural network algorithm that we will discuss in this project.

Project Overview: Time Series Forecasting using LSTM in Python

Deep learning architecture has many branches, and one of them is the recurrent neural network (RNN). The method we will analyze in this deep learning project is Long Short Term Memory Network (LSTM) to perform time series forecasting for univariate time series data.

 

Time Series Forecasting with LSTM Neural Network Python

 

As LSTM is a slightly advanced deep learning algorithm, the project will first introduce simpler neural network algorithms like perceptron to help you understand various neural-networks-related jargon. After that, you will learn different deep learning architectures and utilize LSTM for time series forecasting.

Dataset

The dataset used in this LSTM-python project is an airline’s passengers' data. There are two columns available, one column contains the year and month to represent time, and the other column has information about the number of passengers that traveled in that month.

Tech Stack

Language: Python

Libraries: Pandas, NumPy, Matplotlib, Math, Theano, Keras, scikit-learn

Data Science Concepts Explored in this RNN For Time Series Forecasting Python Project

Here is a list of exciting topics we will cover in this LSTM forecasting Python project.

Deep Learning Architectures

This project covers popular deep learning architectures like deep neural networks, convolutional neural networks, recurrent neural networks, deep belief networks, and the Boltzmann networks. After introducing the basics of these architectures, it will cover essential elements like activation functions, perceptron elements, bias terms, etc. Learning about these elements will help you understand the art of fine-tuning different deep learning algorithms. Additionally, it will assist you in learning how each deep learning algorithm is different from one another.

Setting up the Project Environment

As this RNN time-series forecasting python project will teach you about implementing the LSTM model from scratch, you must know how to install the necessary libraries in your system. And you don’t need to worry about it because the project has a detailed guide for installing all the prerequisite libraries. Additionally, you will learn how to set Theano as the backend library of the Keras framework in Python.

Data Preprocessing

You will learn how to normalize the variables in the dataset using the Python library: sklearn’s functions like MinMaxScaler and StandardScaler. Additionally, you will know how to split the dataset into the test and train subsets and prepare it for the application of deep learning algorithms.

Implementing LSTM in Python for Time Series Forecasting

The Keras framework in Python allows its users to create deep learning models from scratch. In this time series forecasting LSTM python project, you will create all the layers of the LSTM-RNN model using Keras and make predictions for the number of passengers that will fly in the coming years. Furthermore, you will use statistical tools to evaluate the model’s accuracy.

FAQs on Python LSTM Time Series Forecasting

Is LSTM good for Time Series Forecasting?

Yes, LSTM is a good option for forecasting time series data as it is a sequential deep learning model that considers all the values in a given sequence. 

When should I use an RNN LSTM and when to use ARIMA for a time series forecasting problem?

RNN-LSTM model works best for those time series forecasting problems where the relationship between the feature variables and the target variable is non-linear. On the other hand, the ARIMA model works best for situations where the relationship is linear. Additionally, depending on the dataset size, one can pick among the two as LSTMs are computationally expenisve and may take time for large data while ARIMA can be faster in such cases.

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