Time Series (EN)

Description

This course is focused to time series prediction problem.

We begin with examples of classical methods for modeling and prediction of time series and we continue to more advanced methods based on machine learning. We finish with a complex example of training time series model on historical data using neural network and we evaluate its performance in predicting future.

Contents

• Introduction to the theory of time series modeling
• Classical methods for time series prediction (space & frequency domain, spectral analysis, autocorrelation, ARIMA models etc.)
• Hands-on example (pandas, basic characteristics, simple prediction)
• Machine learning for time series prediction (state-space methods, Hidden Markov Chain, Kalman filter, classical neural networks, recurrent networks, LSTM)
• Hands-on examples of machine learning methods (training set preparation for specific task and model, training process & evaluation)
• Complex example of time series prediction using recurrent neural network (temperature prediction from high-dimensional input data: training data set preparation, training process & validation, prediction with trained neural network)

Prerequisites

• basic knowledge of programing in Python
• high school level of mathematics
• Basics of machine learning on the level of our course Introduction to  machine Learning