Please enable JavaScript.
Coggle requires JavaScript to display documents.
Unit 3: Time Series Analysis - Coggle Diagram
Unit 3: Time Series Analysis
behavior
Stationarity
Stationarity defined as constancy over time
Key characteristics: mean, variance, autocorrelation
Simplifies analysis and modeling
Easier forecasting, reliable properties
Non-Stationarity
Non-stationary: changing over time
Varying mean, variance, autocorrelation
Shows trends, seasonality, patterns
Must address before analysis
Avoids spurious correlations, incorrect models
Approaches
Detrending: removing trend
Differencing: eliminate trend/seasonality
Seasonal adjustment: seasonality
Transformation: variance stabilization
ADF test: stationarity determination
Methods Analyzing Time Series
ARIMA models
Autoregressive Component
Moving Average
Integration
SARIMA models
VAR
Types
Reduced form VAR models
Recursive VAR models
Structural VAR models
Advantages
Systematic and flexible
Improved forecasting
Captures intertwined dynamics
Exponential Smoothing
Prophet model
Models include
Curve fitting
Descriptive analysis
Explanative analysis
Exploratory analysis
Forecasting
Intervention analysis
Segmentation
Classification
Time Series
Forecasting:
Box-Jenkins Model (ARIMA: p, d, q)
Rescaled range analysis for trend stability
Cross-sectional analysis for stock evaluation
Models and Techniques:
Box-Jenkins ARIMA models (univariate)
Box-Jenkins Multivariate Models (multivariate)
Holt-Winters Method for seasonal data.