“Time is precious so is Time Series Analysis”

Time series analysis has been around for centuries helping us to solve from astronomical problems to business problems and advanced scientific research around us now. Time stores precious information, which most machine learning algorithms don’t deal with. But time series analysis, which is a mix of machine learning and statistics to help us get useful insights. Time series can be applied to various fields like economy forecasting, budgetary analysis, sales forecasting, census analysis and much more. In this workshop, We will look at how to dive deep into time series data and make use of deep learning to make accurate predictions.

Structure of the workshop goes like this

  • Basics of time series analysis
  • Understanding Time series data with pandas
  • Preprocessing Time Series data
  • Classical Time series models (AR, MA, ARMA, ARIMA, SARIMA, GARCH, E-GARCH)
  • Forecasting with MLP (Multi-Layer Perceptron)
  • Forecasting with RNN (Recurrent Neural Network)
  • Forecasting with LSTM (Long Short Term Memory Network)
  • Understanding Financial Time Series data and forecasting with RNN and LSTM
  • Boosting techniques in Time series data
  • Developing intuition to choose the right network.
  • Dealing with large scale Time Series data



Libraries Used:

  • Keras (with Tensorflow backend)
  • matplotlib
  • pandas
  • statsmodels
  • prophet
  • pyflux
 
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Outline/Structure of the Workshop

1. Introduction to Time series analysis (15 mins)

2. Manipulating time series data using Pandas (30 mins)

3. Time Series exploratory analysis tools (30 mins)

4. Forecast Time series data with some classical method (AR, MA, ARMA, ARIMA, GARCH, E-GARCH) (90 mins)

5. Introduction to deep learning (15 mins)

6. Time series forecasting using MLP, RNN, LSTM (90 mins)

7. Financial Time Series data - (20 Mins)

8. Boosting Techniques - (20 mins)

9. Dealing with Large scale data - (30 mins)

Learning Outcome

- Using Pandas for time series data

- Using classical models for time series forecasting

- Using deep learning for time series forecasting

Target Audience

Audience with interest in time series analysis with a basic understanding of the math behind

Prerequisites for Attendees

Basics of Python

Basics of Time series analysis

Basics of Pandas & Deep learning

The following python packages need to be installed in the laptops of the attendees.

- Pandas

- Keras

- statsmodels

- matplotlib

- Prophet

- Pyflux

schedule Submitted 1 month ago

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