Monograph Articles
Science
Long-form articles and refined explanations in the science track, with filters for topic, series, and difficulty.
From Observations to a Modelable Time Series
Why stationarity matters, how transformations remove changing structure, and what differencing does to the information in a series.
Understanding Dependence in Time Series
How autocovariance, autocorrelation, and linear filters reveal what the past contributes to the present.
ARMA Models as Dynamic Filters
How autoregressive feedback and moving-average innovations create memory, persistence, oscillation, and finite shock effects.
From Correlation Patterns to a Fitted Time-Series Model
How linear projection, PACF, and Yule-Walker equations turn lag relationships into candidate models and forecasts.
Diagnosing and Selecting Time-Series Models
How residual evidence, Ljung-Box testing, AIC, and AICc distinguish useful structure from overfitting.
ARIMA, SARIMA, and Multi-Step Forecasting
How differencing, seasonal structure, and innovation recursions turn persistent time series into forecasts with explicit uncertainty.
Exponential Smoothing as a State-Space Model
How recursive level, trend, and seasonal updates create adaptive forecasts and probabilistic uncertainty.