Generalized Autoregressive Moving Average Models

Benjamin, M.; Rigby, Robert A.; Stasinopoulos, Dimitrios

doi:10.1198/016214503388619238

Cited by 286 publications

(368 citation statements)

References 18 publications

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“…Traditionally, statistical methods like auto regressive moving average (ARMA) and auto regressive integrated moving average (ARIMA) [25] were used for time series regression. However, recently the trend is shifted towards more sophisticated ML models such as different variants of support vector regression (SVR) and artificial neural networks (ANN) because of their robustness and ability to provide more accurate solutions.…”

Section: ) Selection Of Regression Algorithmmentioning

confidence: 99%

Predictive Analytics for Complex IoT Data Streams

Akbar

Khan

Carrez

et al. 2017

IEEE Internet Things J.

111

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Abstract-The requirements of analyzing heterogeneous data streams and detecting complex patterns in near real-time have raised the prospect of Complex Event Processing (CEP) for many internet of things (IoT) applications. Although CEP provides a scalable and distributed solution for analyzing complex data streams on the fly, it is designed for reactive applications as CEP acts on near real-time data and does not exploit historical data. In this regard, we propose a proactive architecture which exploits historical data using machine learning (ML) for prediction in conjunction with CEP. We propose an adaptive prediction algorithm called Adaptive Moving Window Regression (AMWR) for dynamic IoT data and evaluated it using a real-world use case with an accuracy of over 96%. It can perform accurate predictions in near real-time due to reduced complexity and can work along CEP in our architecture. We implemented our proposed architecture using open source components which are optimized for big data applications and validated it on a use-case from Intelligent Transportation Systems (ITS). Our proposed architecture is reliable and can be used across different fields in order to predict complex events.

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Section: ) Selection Of Regression Algorithmmentioning

confidence: 99%

Predictive Analytics for Complex IoT Data Streams

Akbar

Khan

Carrez

et al. 2017

IEEE Internet Things J.

111

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“…the MEM model is a member of the family of Generalized Linear Autoregressive Moving Average (GLARMA) models as pointed by Cipollini et al (2006); for more background on this family we refer to Benjamin et al (2003) and references therein.…”

Section: The Risk Drivermentioning

confidence: 99%

Modelling dynamic portfolio risk using risk drivers of elliptical processes

Schmidt¹,

Schmieder²

2009

Insurance: Mathematics and Economics

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The situation of a limited availability of historical data is frequently encountered in portfolio risk estimation, especially in credit risk estimation. This makes it, for example, difficult to find temporal structures with statistical significance in the data on the single asset level. By contrast, there is often a broader availability of cross-sectional data, i.e., a large number of assets in the portfolio. This paper proposes a stochastic dynamic model which takes this situation into account. The modelling framework is based on multivariate elliptical processes which model portfolio risk via sub-portfolio specific volatility indices called portfolio risk drivers. The dynamics of the risk drivers are modelled by multiplicative error models (MEM) -as introduced by Engle (2002) -or by traditional ARMA models. The model is calibrated to Moody's KMV Credit Monitor asset returns (also known as firm-value returns) given on a monthly basis for 756 listed European companies at 115 time points from 1996 to 2005. This database is used by financial institutions to assess the credit quality of firms. The proposed risk drivers capture the volatility structure of asset returns in different industry sectors. A characteristic temporal structure of the risk drivers, cyclical as well as a seasonal, is found across all industry sectors. In addition, each risk driver exhibits idiosyncratic developments. We also identify correlations between the risk drivers and selected macroeconomic variables. These findings may improve the estimation of risk measures such as the (portfolio) Value at Risk. The proposed methods are general and can be applied to any series of multivariate asset or equity returns in finance and insurance.Key words: Portfolio risk modelling, Elliptical processes, Credit risk, multiplicative error model, volatility clustering, Moody's KMV Credit Monitor database. JEL classification: C51, C16, C13 Non-technical summaryOver the past years, the availability of data for financial analysis in general and portfolio risk analysis in particular has substantially improved. This situation enables the use of more sophisticated methods for portfolio management and risk analysis and has attracted many scholars from the industry, academia, and banking supervision.The present research project proposes a multidimensional stochastic dynamic model that identifies portfolio risk drivers via volatilities in two dimensions, over time and across industry sectors. The identification and the need of modelling volatility dynamics in financial data goes (at least) back to the research by the Nobel laureate Robert Engle and has gained importance during the last two decades. The volatility is often referred to as the key driver of risk in a financial portfolio, and many performance measures or risk measures express the amount of risk via the volatility.The model is applied to market-based credit risk data (monthly asset returns also known as firm-value returns) covering a limited period of time but comprising a large number of assets. T...

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“…Friedlander and Porat 37 proposed an algorithm for estimating the moving average and ARMA parameters of non-Gaussian processes from sample high-order moments. Cox 38 , Lawrance and Lewis 39 and Benjamin et al 40 extended the Gaussian ARMA time-series models to a non-Gaussian framework. In our case study, the resulting time-series from the AF rule can only attain a finite number of outcomes, therefore all of the control charts that are based on normal distributed data are not expected to perform well.…”

Section: Arima-based Spc Chartsmentioning

confidence: 99%

The funnel experiment: The Markov‐based SPC approach

Singer

Ben‐Gal

2007

Quality & Reliability Eng

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The classical funnel experiment was used by Deming to promote the idea of statistical process control (SPC). The popular example illustrates that the implementation of simple feedback rules to stationary processes violates the independence assumption and prevents the implementation of conventional SPC. However, Deming did not indicate how to implement SPC in the presence of such feedback rules. This pedagogical gap is addressed here by introducing a simple feedback rule to the funnel example that results in a nonlinear process to which the traditional SPC methods cannot be applied. The proposed method of Markov-based SPC, which is a simplified version of the context-based SPC method, is shown to monitor the modified process well.

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Generalized Autoregressive Moving Average Models

Cited by 286 publications

References 18 publications

Predictive Analytics for Complex IoT Data Streams

Predictive Analytics for Complex IoT Data Streams

Modelling dynamic portfolio risk using risk drivers of elliptical processes

The funnel experiment: The Markov‐based SPC approach

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