Time Series

• 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

• 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)

Previous courses

Python I - introduction to programming [PYTHON_INTRO]

Introduction to Machine Learning [MLC_INTRO]