Applied Time Series Analysis and Forecasting in Python

Course Overview

• Course Title: Applied Time Series Analysis and Forecasting in Python
• Instructor: Akhil Vydyula
• Target Audience:
  • Beginner data scientists looking to gain experience with time series
  • People interested in quantitative finance
  • Aspiring data scientists
  • Programmers who want to specialize in finance
• Prerequisites: None

Curriculum Highlights

• Key Topics Covered:
  • Special types of time series like White Noise and Random Walks
  • Accounting for "unexpected shocks" via moving averages
  • Coding in Python and using it for statistical analysis
  • Normalizing data when comparing different time series
  • Fundamental time series theory
  • Python libraries: pandas, NumPy, matplotlib, StatsModels, finance, ARCH, prima
  • Models: AR, MA, ARMA, ARIMA, ARIMAX, SARIA, SARIMA, SARIMAX, ARCH, GARCH, VARMA
  • Statistical concepts: stationarity, seasonality, autoregression, moving average, ACF, PACF, Model selection with AIC
  • Deep learning techniques: Simple linear model, DNN, CNN, LSTM, CNN + LSTM models, ResNet, Autoregressive LSTM
• Key Skills Learned:
  • Performing time series analysis
  • Applying statistical models for time series forecasting
  • Using Python for statistical analysis
  • Implementing deep learning techniques for time series analysis
  • Completing end-to-end projects in Python

Course Format

• Duration: 8.5 hours on-demand video
• Format: Self-paced online course
• Resources:
  • 16 downloadable resources
  • Access on mobile and TV
  • Certificate of completion