Foundations of Time Series Analysis by Carlos Polanco (.PDF)

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File Size: 31 MB

Mathematical Foundations of Time Series Analysis: Theory and Cases by Carlos Polanco
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Overview: Mathematical Foundations of Time Series Analysis: Theory and Cases is designed for readers seeking depth and application alike, the book covers foundational ideas such as stationarity, decomposition, trend analysis, and autocorrelation, while also guiding readers through advanced tools like ARIMA models, Kalman filters, and wavelet-based forecasting.

Organized into two distinct parts, the first section introduces the mathematical underpinnings of time series functions, including data behavior patterns, interpolation techniques, and multivariate models. The second section contextualizes these theories through real-world case analyses in areas such as financial risk, epidemiology, price indexing, and seismic activity. Each chapter incorporates examples, stepwise calculations, and remarks that reinforce both conceptual clarity and applied insight.
Genre: Non-Fiction > Educational

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