Mixed Membership Models for Time Series

نویسندگان

  • Emily B. Fox
  • Michael I. Jordan
چکیده

20.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 419 20.1.1 State-Space Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 419 20.1.2 Latent Dirichlet Allocation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 420 20.1.3 Bayesian Nonparametric Mixed Membership Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 420 Hierarchical Dirichlet Process Topic Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 420 Beta-Bernoulli Process Topic Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 422 20.2 Mixed Membership in Time Series . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 425 20.2.1 Markov Switching Processes as a Mixed Membership Model . . . . . . . . . . . . . . . . . . . . . . . . 426 Hidden Markov Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 426 Switching VAR Processes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 426 20.2.2 Hierarchical Dirichlet Process HMMs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 427 20.2.3 A Collection of Time Series . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 428 20.3 Related Bayesian and Bayesian Nonparametric Time Series Models . . . . . . . . . . . . . . . . . . . . . . . . . 434 20.3.1 Non-Homogeneous Mixed Membership Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 434 Time-Varying Topic Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 434 Time-Dependent Bayesian Nonparametric Processes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 434 20.3.2 Hidden-Markov-Based Bayesian Nonparametric Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . 435 20.3.3 Bayesian Mixtures of Autoregressions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 435 20.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 436 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 436

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

A Switchgrass-based Bioethanol Supply Chain Network Design Model under Auto-Regressive Moving Average Demand

Switchgrass is known as one of the best second-generation lignocellulosic biomasses for bioethanol production. Designing efficient switchgrass-based bioethanol supply chain (SBSC) is an essential requirement for commercializing the bioethanol production from switchgrass. This paper presents a mixed integer linear programming (MILP) model to design SBSC in which bioethanol demand is under auto-r...

متن کامل

A New High-order Takagi-Sugeno Fuzzy Model Based on Deformed Linear Models

Amongst possible choices for identifying complicated processes for prediction, simulation, and approximation applications, high-order Takagi-Sugeno (TS) fuzzy models are fitting tools. Although they can construct models with rather high complexity, they are not as interpretable as first-order TS fuzzy models. In this paper, we first propose to use Deformed Linear Models (DLMs) in consequence pa...

متن کامل

مدل بهینه‌سازی طراحی زنجیرۀ تأمین سوخت زیستی تحت تقاضای خودرگرسیون برداری میانگین متحرک

Biofuel is known as one of the best gasoline substitutes in the transportation industry. Designing an optimal supply chain is an essential requirement for the commercialization of biofuel production. This paper presents a mixed integer linear programming model to design a biofuel supply chain in which biofuel demand is under autoregressive moving average time series models. It is studied how th...

متن کامل

Which Methodology is Better for Combining Linear and Nonlinear Models for Time Series Forecasting?

Both theoretical and empirical findings have suggested that combining different models can be an effective way to improve the predictive performance of each individual model. It is especially occurred when the models in the ensemble are quite different. Hybrid techniques that decompose a time series into its linear and nonlinear components are one of the most important kinds of the hybrid model...

متن کامل

Overview and Comparison of Short-term Interval Models for Financial Time Series Forecasting

  In recent years, various time series models have been proposed for financial markets forecasting. In each case, the accuracy of time series forecasting models are fundamental to make decision and hence the research for improving the effectiveness of forecasting models have been curried on. Many researchers have compared different time series models together in order to determine more efficien...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

عنوان ژورنال:

دوره   شماره 

صفحات  -

تاریخ انتشار 2014