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Machine Learning for Timeseries

Machine Learning for Timeseries

A brief summary of the topics covered in this course is as below. This is 24 hours course, it is suggested to complete this course in 3 weeks. Apart from my classroom course, you will be given exercises, and it will take another 100 hours in the course duration to complete these exercises.

Introduction to Timeseries

  • What is a time series?
  • Time-dependent seasonal components.
  • Autocorrelation
  • Seasonality
  • Stationarity
  • Autoregressive (AR),
  • Moving average (MA) and mixed ARMA- Modeler.
  • Autoregressive Integrated Moving Average Model (ARIMA)
  • Seasonal autoregressive integrated moving average model (SARIMA)
  • Autocorrelation function (ACF)
  • Partial Autocorrelation function (PACF)
  • The random walk model.
  • Box-jenkins methodology.
  • Vector Auto Regression (VAR) models.
  • Dynamic models with time-shifted explanatory variables.
  • The koyck transformation.
  • Granger’s causality tests.
  • Stationarity, unit roots and integration
  • Time series model performance
  • Various approach to solve time series problem
  • Complete end-to-end project with deployment, nifty stock price prediction and deployment.


  • Introduction to forecasting : Purpose, process, components of a time series, overfitting and data partitioning
  • Model building: Level, linear trend, quadratic trend, exponential trend, additive seasonality, multiplicative seasonality, combining trend & seasonality, forecasting future data
  • ARIMA model – Lag analysis, Model building, implementation

Deep Learning for Time Series

  • RNN (Recurrent Neural Network)
  • GRU (Gated Recurrent Unit)
  • LSTM (Long Short-term Memory)
  • Bidirectional RNN

Time Series Libraries

SKTIME TSfresh Prophet AutoTS DarTS

Automated Time Series Models in Python (AtsPy)