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Time Series Analysis and Forecasting

Chapter 1: Introduction to Time Series Data

Characteristics of Time Series Data

Components: Trend, Seasonality, Cyclical, Irregular

Loading and Handling Time Series in Pandas

Time Shifting, Lagging, and Rolling Windows

Visualizing Time Series Data

Hands-on Practice: Loading and Plotting Data

Chapter 2: Time Series Decomposition and Stationarity

Understanding Stationarity

Methods for Time Series Decomposition

Implementing Decomposition in Python

Testing for Stationarity: Visual Inspection

Statistical Tests for Stationarity (ADF Test)

Achieving Stationarity: Differencing

Hands-on Practice: Decomposition and Stationarity Tests

Chapter 3: Autocorrelation and Model Identification

Autocorrelation Function (ACF)

Partial Autocorrelation Function (PACF)

Plotting ACF and PACF in Python

Interpreting ACF/PACF for Model Selection

Hands-on Practice: ACF/PACF Plotting and Interpretation

Chapter 4: ARIMA Models for Forecasting

Autoregressive (AR) Models

Moving Average (MA) Models

Combining AR and MA: ARMA Models

Introducing Integration: ARIMA Models

Selecting ARIMA Order (p, d, q)

Fitting ARIMA Models in Python (statsmodels)

Model Diagnostics and Residual Analysis

Forecasting with ARIMA Models

Hands-on Practice: Building an ARIMA Model

Chapter 5: Handling Seasonality with SARIMA

Limitations of ARIMA with Seasonal Data

Introduction to Seasonal ARIMA (SARIMA)

Identifying Seasonal Components (ACF/PACF)

Selecting SARIMA Order (p, d, q)(P, D, Q)m

Fitting SARIMA Models in Python

Diagnosing SARIMA Models

Forecasting with SARIMA

Hands-on Practice: Building a SARIMA Model

Chapter 6: Model Evaluation and Selection

Need for Model Evaluation

Train-Test Split for Time Series

Common Evaluation Metrics (MAE, MSE, RMSE, MAPE)

Information Criteria (AIC, BIC)

Comparing Forecasts from Different Models

Visualizing Forecast Performance

Hands-on Practice: Evaluating Forecasts

Time Series Analysis and Forecasting

Prerequisites: Familiarity with Python programming, Pandas library, and basic statistical concepts.

Level: Intermediate

What You'll Learn

Time Series Decomposition
Decompose time series data into trend, seasonality, and residual components.
Stationarity Testing
Understand and test for stationarity using statistical tests like ADF.
Autocorrelation Analysis
Analyze autocorrelation and partial autocorrelation functions (ACF/PACF) to identify model parameters.
ARIMA Modeling
Build and evaluate Autoregressive Integrated Moving Average (ARIMA) models for forecasting.
Seasonal ARIMA (SARIMA)
Implement SARIMA models to handle seasonal patterns in time series data.
Model Evaluation
Evaluate the performance of forecasting models using appropriate metrics.

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