The highly prized ability to make financial plans with some certainty about the future comes from the core fields of economics. In recent years the availability of more data, analytical tools of greater precision, and ex post studies of business decisions have increased demand for information about economic forecasting. Volumes 2A and 2B, which follows Nobel laureate Clive Granger's Volume 1 (2006), concentrate on two major subjects. Volume 2A covers innovations in methodologies, specifically macroforecasting and forecasting financial variables. Volume 2B investigates commercial applications, with sections on forecasters' objectives and methodologies. Experts provide surveys of a large range of literature scattered across applied and theoretical statistics journals as well as econometrics and empirical economics journals. The Handbook of Economic Forecasting Volumes 2A and 2B provide a unique compilation of chapters giving a coherent overview of forecasting theory and applications in one place and with up-to-date accounts of all major conceptual issues. It focuses on innovation in economic forecasting via industry applications. It presents coherent summaries of subjects in economic forecasting that stretch from methodologies to applications. It makes details about economic forecasting accessible to scholars in fields outside economics.

Handbook of Economic Forecasting

Short-term traffic flow prediction has long been regarded as a critical concern for intelligent transportation systems. On the basis of many existing prediction models, each having good performance only in a particular period, an improved approach is to combine these single predictors together for prediction in a span of periods. In this paper, a neural network model is introduced that combines the prediction from single neural network predictors according to an adaptive and heuristic credit assignment algorithm based on the theory of conditional probability and Bayes' rule. Two single predictors, i.e., the back propagation and the radial basis function neural networks are designed and combined linearly into a Bayesian combined neural network model. The credit value for each predictor in the combined model is calculated according to the proposed credit assignment algorithm and largely depends on the accumulative prediction perfor- mance of these predictors during the previous prediction intervals. For experimental test, two data sets comprising traffic flow rates in 15-min time intervals have been collected from Singapore's Ayer Rajah Expressway. One data set is used to train the two single neural networks and the other to test and compare the performances between the combined and singular models. Three indices, i.e., the mean absolute percentage error, the variance of absolute percentage error, and the probability of percentage error, are employed to compare the forecasting performance. It is found that most of the time, the combined model outperforms the singular predictors. More importantly, for a given time period, it is the role of this newly proposed model to track the predictors' performance online, so as to always select and combine the best-performing predictors for prediction.

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Short-term freeway traffic flow prediction : Bayesian combined neural network approach

Models for mid-term electricity demand forecasting incorporating weather influences

Time series are recorded values of an interesting phenomenon such as stock prices, household incomes, or patient heart rates over a period of time. Time series data mining focuses on discovering interesting patterns in such data. This article introduces a wavelet-based time series data analysis to interested readers. It provides a systematic survey of various analysis techniques that use discrete wavelet transformation (DWT) in time series data mining, and outlines the benefits of this approach demonstrated by previous studies performed on diverse application domains, including image classification, multimedia retrieval, and computer network anomaly detection.

Discrete wavelet transform-based time series analysis and mining

The Bayesian combination method (BCM) proposed by Petridis et al. (2001) is an integrated method that can effectively improve the predictions of single predictors. However, research has found that it considers redundant prediction errors of component predictors when calculating their credits, which makes it quite impervious to the fluctuated accuracy of the component predictors. To address this problem, a new BCM has been developed here to improve the performance of the traditional BCM. It assumes that at one prediction interval, the traffic flow is correlated with the traffic flows of only a few previous intervals. With this assumption, the credits of the component predictors in the BCM are only accounted for by their prediction performance for a few intervals rather than for all intervals. Therefore, compared with the traditional BCM, the new BCM is more sensitive to the perturbed performance of the component predictors and can adjust their credits more rapidly, and better predictions are generated as a result. To analyze the relevancy between the historical traffic flows and the traffic flow at the current interval, the entropy-based grey relation analysis method is proposed in detail. Three single predictors, namely the autoregressive integrated moving average (ARIMA), Kalman filter (KF) and back propagation neural network (BPNN) are designed and incorporated linearly into the BCM to take advantage of each method. A numerical application demonstrates that the new BCM considerably outperforms the traditional BCM both in terms of accuracy and stability.

New Bayesian combination method for short-term traffic flow forecasting

In this paper we present the Bayesian Combined Predictor (BCP), a probabilistically motivated predictor for time series prediction. BCP utilizes local predictors of several types (e.g., linear predictors, artificial neural network predictors, polynomial predictors etc.) and produces a final prediction which is a weighted combination of the local predictions; the weights can be interpreted as Bayesian posterior probabilities and are computed online. Two examples of the method are given, based on real world data: (a) short term load forecasting for the Greek Public Power Corporation dispatching center of the island of Crete, and (b) prediction of sugar beet yield based on data collected from the Greek Sugar Industry. In both cases, the BCP outperforms conventional predictors.

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H. Panagiotou

Papers

A Bayesian Multiple Models Combination Method for Time Series Prediction

Predictive Modular Neural Networks Methods for Prediction of Sugar Beet Crop Yield