Applied Bayesian Forecasting and Time Series Analysis

Pole, Andy; West, Mike; Harrison, Jeff

doi:10.1201/9781315274775

Cited by 36 publications

(27 citation statements)

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“…Can Bayesian methods be used for time-series analysis? Yes: (e.g., Geweke & Whiteman, 2006;McCulloch & Tsay, 1994;Pole, West, & Harrison, 1994). Can Bayesian methods be used for hierarchical conditional-logistic models of nominal data?…”

Section: Bayesian Analysis Applies To Any Parameterized Model Of Datamentioning

confidence: 99%

Bayesian data analysis for newcomers

2017

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This article explains the foundational concepts of Bayesian data analysis using virtually no mathematical notation. Bayesian ideas already match your intuitions from everyday reasoning and from traditional data analysis. Simple examples of Bayesian data analysis are presented, that illustrate how the information delivered by a Bayesian analysis can be directly interpreted. Bayesian approaches to null-value assessment are discussed. The article clarifies misconceptions about Bayesian methods that newcomers might have acquired elsewhere. We discuss prior distributions and explain how they are not a liability but an important asset. We discuss the relation of Bayesian data analysis to Bayesian models of mind, and we briefly discuss what methodological problems Bayesian data analysis is not meant to solve. After you have read this article, you should have a clear sense of how Bayesian data analysis works and the sort of information it delivers, and why that information is so intuitive and useful for drawing conclusions from data.

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Section: Bayesian Analysis Applies To Any Parameterized Model Of Datamentioning

confidence: 99%

Bayesian data analysis for newcomers

2017

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“…For this research, Dynamic Models (DMs) have been chosen. Unlike static models, some elements that participate in the construction of the model do not remain invariable but are considered as functions of time describing temporal trajectories (Glynn et al, 2019;Laine, 2019;McAlinn & West, 2019;Pole, West, & Harrison, 2018).…”

Section: Methodology and Datamentioning

confidence: 99%

Air Traffic Forecast in Post-Liberalization Context: A Dynamic Linear Models Approach

2020

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The process of air transport liberalization in Colombia began in 1991. Liberalization entailed the entry of private capital into the airport sector which subsequently led, in several temporary phases, to the privatization of the country’s main airports. Simultaneously, new air operators entered the market. This new market situation, supported by the complete deregulation of airfares, generated a dynamic and sustained growth of air transport in Colombia for two decades. Within the context of post-liberalization, this article presents a forecast (medium-term – 5 years period) of air traffic in the country’s main airport using DLMs (Dynamic Linear Models). It has the following advantages vs. the usual forecast calculation methodologies: it detects stochastic tendencies that are hidden in the time series. It also detects structural changes that allow estimating the variable effect of exogenous shocks over time without increasing the number of parameters. From the results obtained, it should be noted that the application of DLMs presents MAPE (Mean Absolute Percentage Error) values below 1%, which guarantees predictions of higher accuracy and thus introduces a new alternative model to develop reliable forecasts in air transport, at least in the medium-term.

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“…Increasing computational speed allowed for the development and use of exact statistical tests, many of which do not require knowledge of underlying distributions. Further increases in the power of desktop computers, and improved methods of numerical analysis and approximation, have meant that Bayesian software can now be implemented more easily (e.g., Pole et al 1994, Cook and Broemeling 1995, Albert 1996. Not surprisingly given these tools, a careful examination of assumptions of, and similarities and differences between Bayesian and frequentist inference has led to a greater diversity of approaches in the major statistical journals.…”

Section: "The" Scientific Methodsmentioning

confidence: 99%

Statistics and the Scientific Method in Ecology

Dennis

2004

The Nature of Scientific Evidence

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is difficult and progress is slow, due to the variability of natural systems and the high cost of obtaining good data. Ecology however is charged with providing information support for environmental policy decisions with far reaching societal consequences. Demand for quick answers is strong, and demand for answers that agree with a particular point of view is even stronger.The use of Bayesian statistical analyses has recently been advocated in ecology, supposedly to aid decision makers and enhance the pace of progress. Bayesian statistics provides conclusions in the face of incomplete information. Bayesian statistics, though, represents a much different approach to science than the frequentist statistics studied by most ecologists. The scientific implications of Bayesian statistics are not well understood.I provide a critical review of the Bayesian approach. I compare, using a simple sampling example, the Bayesian and frequentist analyses. The Bayesian analyses can be "cooked" to produce results consistent with any point of view, because Bayesian analyses quantify prior personal beliefs and mix them with the data. In this, Bayesian statistics is consistent with the postmodern view of science, widely held among nonscientists, in which science is just a system of beliefs that has no particular authority over any other system of beliefs. By contrast, modern empirical science uses the scientific method to identify empirical contradictions in skeptics' beliefs and permit replication and checking of empirical results. Frequentist statistics has become an indispensible part of the scientific method.I also undertake a critical discussion of statistics education in ecology. Part of the potential appeal of Bayesian statistics is that many ecologists are confused about frequentist statistics, and statistical concepts in general. I identify the source of confusion as arising from ecologists' attempts to learn statistics through a series of precalculus "statistical methods" courses taken in graduate school. I prescribe a radical change in the statistical training of ecological scientists which will greatly increase the level of confidence and facility with statistical thinking. 2. Ecology as a science is under constant political pressure. The science is Abstract difficult and progress is slow, due to the variability of natural systems and the high cost of obtaining good data. Ecology however is charged with providing information support for environmental policy decisions with far reaching societal consequences. Demand for quick answers is strong, and demand for answers that agree with a particular point of view is even stronger.The use of Bayesian statistical analyses has recently been advocated in ecology, supposedly to aid decision makers and enhance the pace of progress. Bayesian statistics provides conclusions in the face of incomplete information. Bayesian statistics, though, represents a much different approach to science than the frequentist statistics studied by most ecologists. The scientific implications o...

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Applied Bayesian Forecasting and Time Series Analysis

Cited by 36 publications

References 0 publications

Bayesian data analysis for newcomers

Bayesian data analysis for newcomers

Air Traffic Forecast in Post-Liberalization Context: A Dynamic Linear Models Approach

Statistics and the Scientific Method in Ecology

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