CQ-AI/

time_series_notebooks

What’s Inside

• Exploratory Data Analysis (EDA)
  Techniques for visualizing, decomposing, and understanding temporal structures in financial time series.
• Feature Engineering for Time Series
  Lag features, rolling statistics, seasonal indicators, and date-based encodings.
• Classical Forecasting Methods
  • ARIMA / SARIMA
  • Facebook Prophet
  • Vector Auto Regression
  • Arch/Garch for volatility modeling
  • Single and Double Exponential Smoothing
  • Holt Winters Exponential Smoothing
• Machine Learning Approaches
  • Random Forests
  • XGBoost
  • Long Short Term Memory
• Model Optimization and Evaluation
  Grid-search-cv , Randomized-search-cv, Training with cross-validation, and performance metrics (MAE, RMSE, MAPE).
• Additional concpets covered Grangers causality test, Parameter selection with AIC , BIC

Datasets

• Stock price data.
• Commodity Prices.
• Foreign Exchnage rates.
• Inflation rates.
• Cryptocurrency price histories.
• Sales and Revenue datasets