{"product_id":"time-series-analytics-foundations-and-linear-models-volume-i-9798259382367","title":"Time Series Analytics: Foundations and Linear Models - Volume I","description":"\u003cp\u003e • Author(s): Amit Dua\u003cbr\u003e • Publisher: Independently Published\u003cbr\u003e • Publisher Imprint: Independently Published\u003cbr\u003e • BISAC: Econometrics\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003cb\u003eVolume I of a complete graduate course in time series analytics. Foundations and Linear Models in eighteen rigorous chapters, with theorems, full proofs, and runnable Python on real markets.\u003c\/b\u003e\u003c\/p\u003e\u003cp\u003eThis is the textbook the author wished he had when first standing in front of a quantitative graduate audience. The literature splits, awkwardly, into rigorous monographs that assume more probability theory than most applied students bring; econometrics texts that move past foundations to applications; and software-first introductions that get students producing forecasts but unable to defend a single line of the underlying derivation. \u003ci\u003eTime Series Analytics: Theory and Python Practice\u003c\/i\u003e sits in a different middle. Every chapter combines formal theory with working code.\u003c\/p\u003e\u003cp\u003e\u003cb\u003eVolume I covers: \u003c\/b\u003e\u003c\/p\u003e\u003cul\u003e\n\u003cli\u003e\n\u003cb\u003eFoundations\u003c\/b\u003e (Chapters 1-8): stochastic processes; classical decomposition and STL; smoothing filters and exponential smoothing; the autocorrelation function; portmanteau tests for noise; variance-stabilising transformations.\u003c\/li\u003e\n\u003cli\u003e\n\u003cb\u003eLinear models\u003c\/b\u003e (Chapters 9-18): the Wold decomposition; backshift-operator calculus; the MA, AR, ARMA, ARIMA, and SARIMA families with full Box-Jenkins identification; transfer-function models; and two end-of-volume Python applications on Indian-equity data (MRF Limited daily ARIMA; NIFTY 50 monthly SARIMA).\u003c\/li\u003e\n\u003c\/ul\u003e\u003cp\u003e\u003cb\u003eWhat makes this different: \u003c\/b\u003e\u003c\/p\u003e\u003cul\u003e\n\u003cli\u003e\n\u003cb\u003eTwo theorems per chapter, with full proofs\u003c\/b\u003e in the Brockwell-Davis tradition. No \"as the reader can show\".\u003c\/li\u003e\n\u003cli\u003e\n\u003cb\u003eTwo worked examples per chapter\u003c\/b\u003e: one synthetic so the student can verify by hand, one drawn from a real Indian-equity series - NIFTY 50, NIFTY Bank, MRF, Reliance Industries.\u003c\/li\u003e\n\u003cli\u003e\n\u003cb\u003eFive graded discussion questions per chapter\u003c\/b\u003e, with full worked solutions in the back-matter Solutions Appendix.\u003c\/li\u003e\n\u003cli\u003e\n\u003cb\u003ePinned numerical results.\u003c\/b\u003e Every empirical claim is pinned to the exact output of a seeded Python run. Reproduce with \u003ci\u003enp.random.seed(42)\u003c\/i\u003e.\u003c\/li\u003e\n\u003c\/ul\u003e\u003cp\u003e\u003cb\u003eFor graduate students\u003c\/b\u003e in statistics, financial engineering, applied mathematics, and quantitative economics - and for the practitioner who wants both the theory and the working code in a single self-contained text.\u003c\/p\u003e\u003cp\u003e\u003cb\u003ePrerequisites: \u003c\/b\u003e probability through the central limit theorem; linear algebra including Cholesky decomposition; idiomatic NumPy and pandas. No prior exposure to \u003ci\u003estatsmodels\u003c\/i\u003e, \u003ci\u003earch\u003c\/i\u003e, or \u003ci\u003eyfinance\u003c\/i\u003e is assumed.\u003c\/p\u003e\u003cp\u003e\u003cb\u003eVolume II\u003c\/b\u003e (sold separately) covers Estimation, Forecasting and Diagnostics; Multivariate and Volatility Models (VAR, Granger causality, IRFs, cointegration, ARCH\/GARCH\/EGARCH\/GJR-GARCH and Value-at-Risk); and four end-to-end Python projects on Bitcoin volatility and a Gold-Bitcoin VAR.\u003c\/p\u003e\u003cp\u003eApproximately 495 pages - 18 chapters - 90 worked discussion-question solutions - dataset whitelist (NIFTY 50, NIFTY Bank, MRF, Reliance) - full reproducibility under np.random.seed(42).\u003c\/p\u003e","brand":"Independently Published","offers":[{"title":"Paperback","offer_id":47883126833303,"sku":"9798259382367","price":2955.0,"currency_code":"INR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0666\/3471\/1191\/files\/9798259382367.webp?v=1781099662","url":"https:\/\/atlanticbooks.com\/products\/time-series-analytics-foundations-and-linear-models-volume-i-9798259382367","provider":"Atlantic Books","version":"1.0","type":"link"}