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Forecasting with Dynamic Regression Models

This volume presents the basic concepts and practice of building, using and interpreting single equation dynamic regression models (also;

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Applied Economic Forecasting using Time Series Methods

forecasting problems. Applied Economic Forecasting using Time Series Methods starts with a brief review of basic regression analysis with a focus on;

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Supervised Machine Learning in Wind Forecasting and Ramp Event Prediction

generation and forecasting, with a focus on the role and need of Machine Learning in this emerging field of knowledge. Various regression models and;

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SAP Predictive Analytics The Comprehensive Guide Rheinwerk Publishing

Leave your crystal ball behind and peer into the future with SAPPredictive Analytics. Master predictive models-regression, time series;

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Bayesian Forecasting and Dynamic Models

This text is concerned with Bayesian learning, inference and forecasting in dynamic environments. We describe the structure and theory;

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Regression Analysis and Linear Models

understand language. Such selected concepts that redefine the subject have been presented herein. Those with an interest in the field of regression;

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Statistical Learning for Big Dependent Data

Master advanced topics in the analysis of large, dynamically dependent datasets with this insightful resource Statistical Learning with;

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Handbook of Economic Forecasting

of Economic Forecasting . The handbook covers developments in how forecasts are constructed based on multivariate time-series models, dynamic factor;

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Demographic Forecasting

explanatory variables in a time-series regression for each cross section while still borrowing strength from one regression to improve the estimation;

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Time Series Analysis for the Social Sciences

of their data as a nuisance rather than a substantively meaningful dynamic process to be modeled and interpreted. Time Series Analysis for the;

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Dynamic Regression Models for Survival Data

This book studies and applies modern flexible regression models for survival data with a special focus on extensions of the Cox model and;

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Dynamic Regression Models for Survival Data

This book studies and applies modern flexible regression models for survival data with a special focus on extensions of the Cox model and;

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Modeling in Mathematics

representing noisy sounds with short-time sinusoids, robust Bayesian regularized estimation based on regression model and robust quadratic regression;

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An Introduction to Time Series Analysis and Forecasting

of forecast improvement with regression and autoregression combination models and model and forecast evaluation, along with a sample size analysis for;

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Dealing with Endogeneity in Regression Models with Dynamic Coefficients

Dealing with Endogeneity in Regression Models with Dynamic Coefficients presents a unified econometric framework for dealing with the;

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Hands-On Time Series Analysis with R

regression models with machine learning algorithms such as Random Forest and Gradient Boosting Machine using the h2o package. By the end of this book;

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Applied Regression Modeling

Master the fundamentals of regression without learning calculus with this one-stop resource The newly and thoroughly revised 3rd;

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Electrical Load Forecasting

, their advantages and disadvantages and when they are best utilized.The book begins with a good description of the basic theory and models needed;

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Introduction To Time Series Analysis And Forecasting

the gap between theory and practice by equipping readers with the tools needed to analyze time-oriented data and construct useful, short- to;

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Forecasting, Time Series, and Regression (with CD-ROM)

Awarded Outstanding Academic Book by CHOICE magazine in its first edition, FORECASTING, TIME SERIES, AND REGRESSION: AN APPLIED APPROACH;

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Econometrics

builds from the classical regression model to cover large sample theory, disturbance problems and generalised least squares, formation estimation;

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Yield Curve Modeling and Forecasting

Understanding the dynamic evolution of the yield curve is critical to many financial tasks, including pricing financial assets and their;

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Practical Business Forecasting

, requiring only a familiarity with basic statistics. The text focuses on the use of models in forecasting, explaining how to build practical;

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Practical Business Forecasting

, requiring only a familiarity with basic statistics. The text focuses on the use of models in forecasting, explaining how to build practical;

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Hybrid models for Hydrological Forecasting: integration of data-driven and conceptual modelling techniques

models with data-driven models for the purpose of hydrological flow forecasting. Models resulting from such integration are referred to as hybrid;

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Advanced Forecasting with Python

with reflections on model selection such as benchmark scores vs. understandability of models vs. compute time, and automated retraining and;

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Simultaneous Inference in Regression

assess part of a multiple linear regression model with the zero function, to compare two regression models, and to evaluate more than two;

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