289,84 €
340,99 €
-15% su kodu: ENG15
An Introduction to Time Series Analysis and Forecasting
289,84 € 
- Išsiųsime per 10–14 d.d.
Providing a clear explanation of the fundamental theory of time series analysis and forecasting, this book couples theory with applications of two popular statistical packages--SAS and SPSS. The text examines moving average, exponential smoothing, Census X-11 deseasonalization, ARIMA, intervention, transfer function, and autoregressive error models and has brief discussions of ARCH and GARCH models. The book features treatments of forecast improvement with regression and autoregression combinat…
289.84
2025-07-13 23:59:00
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- Autorius: Robert A Yaffee
- Leidėjas: Academic Press
- ISBN-10: 0127678700
- ISBN-13: 9780127678702
- Formatas: 15 x 23.4 x 3.3 cm, kieti viršeliai
- Kalba: Anglų
- Extra -15 % nuolaida šiai knygai su kodu: ENG15
An Introduction to Time Series Analysis and Forecasting + nemokamas atvežimas! | knygos.lt
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Providing a clear explanation of the fundamental theory of time series analysis and forecasting, this book couples theory with applications of two popular statistical packages--SAS and SPSS. The text examines moving average, exponential smoothing, Census X-11 deseasonalization, ARIMA, intervention, transfer function, and autoregressive error models and has brief discussions of ARCH and GARCH models. The book features treatments of forecast improvement with regression and autoregression combination models and model and forecast evaluation, along with a sample size analysis for common time series models to attain adequate statistical power. The careful linkage of the theoretical constructs with the practical considerations involved in utilizing the statistical packages makes it easy for the user to properly apply these techniques.EXTRA 15 % nuolaida su kodu: ENG15
289,84 €
340,99 €
Išsiųsime per 10–14 d.d.
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- Autorius: Robert A Yaffee
- Leidėjas: Academic Press
- ISBN-10: 0127678700
- ISBN-13: 9780127678702
- Formatas: 15 x 23.4 x 3.3 cm, kieti viršeliai
- Kalba: Anglų
Providing a clear explanation of the fundamental theory of time series analysis and forecasting, this book couples theory with applications of two popular statistical packages--SAS and SPSS. The text examines moving average, exponential smoothing, Census X-11 deseasonalization, ARIMA, intervention, transfer function, and autoregressive error models and has brief discussions of ARCH and GARCH models. The book features treatments of forecast improvement with regression and autoregression combination models and model and forecast evaluation, along with a sample size analysis for common time series models to attain adequate statistical power. The careful linkage of the theoretical constructs with the practical considerations involved in utilizing the statistical packages makes it easy for the user to properly apply these techniques.
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