Time Series Track

A step-by-step path through time series analysis in Python: decomposition and STL, stationarity tests (ADF, KPSS) and white noise vs random walks.

Read the notebooks in order: each part builds on the one before it. Every part is a runnable Jupyter notebook with the code, outputs and charts included.

You’ll learn: how to break a series into trend, seasonality and noise; how to test and fix non-stationarity; and how to fit and compare AR, MA, ARMA, ARIMA and SARIMA forecasting models.

Prerequisites: Python and pandas basics, and introductory statistics (mean, variance, correlation).

Tools: pandas, NumPy, statsmodels, scikit-learn, matplotlib, yfinance.

Roadmap

  1. Part 1

    Time Series Analysis and Decomposition

    Learn the fundamentals of time series forecasting, additive vs. multiplicative models, and robust STL decomposition using NIFTY 50 data.

    Time SeriesFinanceData VisualizationPython

  2. Part 2

    Stationarity in Time Series Analysis

    Learn how to detect non-stationarity in time series using statistical tests and apply techniques like differencing, transformations, and detrending to achieve stationarity.

    Time SeriesStationarityPython

  3. Part 3

    White Noise and Random Walks in Time Series Analysis

    Learn how to identify White Noise and Random Walk processes in financial time series using visual, ACF/PACF, and Ljung-Box test diagnostics.

    Time SeriesFinanceStatisticsPython

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