MathIsimple

Time Series Analysis

Browse focused sections below — each card opens a topic page for step-by-step reading and practice.

🎯 Core Modules

Structured modules that guide you from theory to practice

Available!
📊
Advanced
Stationary Processes
Master weak stationarity, ACF, white noise, linear processes, spectral analysis, and ergodicity with rigorous proofs.
Duration: 2-3 hours
  • Weak Stationarity
  • ACF & Spectral Density
  • Wold Decomposition
  • Ergodic Theorems
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📈
Advanced
Autoregressive Models
Deep dive into AR processes: lag operators, difference equations, Yule-Walker estimation, spectral density, and PACF diagnostics.
Duration: 2-3 hours
  • Lag Operators
  • Yule-Walker Equations
  • PACF & Model Selection
  • Forecasting Theory
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Available!
🌊
Advanced
Moving Average Models
Explore MA(q) processes: invertibility, q-step correlation, parameter estimation (Newton-Raphson), and duality with AR models.
Duration: 2-3 hours
  • Invertibility Condition
  • q-Step Correlation
  • Parameter Estimation
  • Spectral Analysis
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Available!
🔀
Advanced
ARMA Models
Master mixed models combining AR and MA: parsimony principle, EACF identification, MLE estimation, and recursive forecasting.
Duration: 3-4 hours
  • Pars imony Principle
  • EACF & Model Selection
  • MLE Estimation
  • Recursive Forecasting
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Available!
📊
Advanced
Statistical Inference & Estimation
Master parameter estimation theory: mean and autocovariance estimation, consistency, asymptotic distributions, CLT, LIL, and white noise testing.
Duration: 3-4 hours
  • Mean Estimation
  • Autocovariance Est.
  • CLT & LIL
  • White Noise Tests
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Time Series Analysis - Deterministic & Stochastic Decomposition | MathIsimple