Run this notebook: Open in Colab Open in Kaggle

Phase 26: Time Series Analysis — Start Here¶

Analyze and forecast sequential data — from stock prices to sensor readings to web traffic — using classical statistics and modern deep learning.

What Is Time Series Analysis?¶

Time series are observations ordered in time. The goal is to:

Understand patterns: trend, seasonality, cycles
Forecast future values
Detect anomalies in real time

Notebooks in This Phase¶

Notebook	Topic
`01_time_series_fundamentals.ipynb`	Stationarity, autocorrelation, decomposition
`02_classical_statistical_methods.ipynb`	ARIMA, SARIMA, Exponential Smoothing
`03_facebook_prophet.ipynb`	Prophet for business forecasting
`04_deep_learning_time_series.ipynb`	LSTM, Temporal Convolutional Networks, Transformers
`05_advanced_techniques_applications.ipynb`	Anomaly detection, multivariate forecasting
`06_practical_applications_exercises.ipynb`	Real datasets: stock, weather, electricity

Methods Overview¶

Method	Strengths	When to Use
ARIMA	Interpretable, few params	Short univariate series
Prophet	Handles holidays, seasonality	Business KPIs
LSTM	Long-range dependencies	Complex multivariate data
Transformers	Parallelizable, long context	Large-scale forecasting
N-BEATS / N-HiTS	State-of-the-art accuracy	Production forecasting

Prerequisites¶

Python and NumPy/Pandas
Basic statistics
Neural Networks helpful for DL notebooks

Learning Path¶

01_time_series_fundamentals.ipynb      ← Start here
02_classical_statistical_methods.ipynb
03_facebook_prophet.ipynb              ← Practical and fast
04_deep_learning_time_series.ipynb
05_advanced_techniques_applications.ipynb
06_practical_applications_exercises.ipynb