: This thesis applies neural network feature selection techniques to multivariate time series data to improve prediction of a target time series. Two approaches to feature selection are used. First, a subset enumeration method is used to determine which financial indicators are most useful for aiding in prediction of the S&P 500 futures daily price. The candidate indicators evaluated include RSI, Stochastics and several moving averages. Results indicate that the Stochastics and RSI indicators result in better prediction results than the moving averages. The second approach to feature selection is calculation of individual saliency metrics. A new decision boundary-based individual saliency metric, and a classifier independent saliency metric are developed and tested. Ruck's saliency metric, the decision boundary based saliency metric, and the classifier independent saliency metric are compared for a data set consisting of the RSI and Stochastics indicators as well as delayed closing price values. The decision based metric and the Ruck metric results are similar, but the classifier independent metric agrees with neither of the other metrics. The nine most salient features, determined by the decision boundary based metric, are used to train a neural network and the results are presented and compared to other published results. (AN)

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Nonlinear Time Series Analysis.

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Analysis of observed chaotic data

El Niño, La Niña, and the Southern Oscillation

Теория и практика исследований в области биологии и промышленного культивирования съедобных грибов на кафедре микологии и альгологии Московского Государственного Университета им

Author(s): Gopinath, G; Boz, E; Casas, C; Diez, FJ; Gourinchas, PO; Plagborg-Moller, M | Abstract: We propose a “dominant currency paradigm” with three key features: dominant currency pricing, pricing complementarities, and imported inputs in production. We test this paradigm using a new dataset of bilateral price and volume indices for more than 2,500 country pairs that covers 91 percent of world trade, as well as detailed firm-product-country data for Colombian exports and imports. In strong support of the paradigm we find that (i) noncommodities terms-of-trade are uncorrelated with exchange rates; (ii) the dollar exchange rate quantitatively dominates the bilateral exchange rate in price pass-through and trade elasticity regressions, and this effect is increasing in the share of imports invoiced in dollars; (iii) US import volumes are significantly less sensitive to bilateral exchange rates, compared to other countries’ imports; (iv) a 1 percent US dollar appreciation against all other currencies predicts a 0.6 percent decline within a year in the volume of total trade between countries in the rest of the world, controlling for the global business cycle. We characterize the transmission of, and spillovers from, monetary policy shocks in this environment.

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Dominant Currency Paradigm

What explains the central role of the dollar in world trade? Will the US currency retain its dominant status in the future? This paper develops a quantitative general equilibrium framework with endogenous currency choice that can address these questions. Complementarities in price setting and input-output linkages across firms generate complementarities in currency choice making exporters coordinate on the same currency of invoicing. The dollar is more likely to play this role because of the large size of the US economy, a widespread peg to the dollar, and the history dependence in currency choice. Calibrated using the world input-output tables and exchange rate moments, the model can successfully replicate the key empirical facts about the use of currencies at the global level, across countries, and over time. According to the counterfactual analysis, the peg to the dollar in other economies ensures that the US currency is unlikely to lose its global status because of the falling US share in the world economy, but can be replaced by the renminbi in case of a negative shock in the US economy. If the peg is abandoned, the world is likely to move to a new equilibrium with multiple regional currencies. (JEL D21, E31, E42, F14, F31, F33)

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An Equilibrium Model of the International Price System

We suggest a new nonlinear expansion of space-distributed observational time series. The expansion allows constructing principal nonlinear manifolds holding essential part of observed variability. It yields low-dimensional hidden time series interpreted as internal modes driving observed multivariate dynamics as well as their mapping to a geographic grid. Bayesian optimality is used for selecting relevant structure of nonlinear transformation, including both the number of principal modes and degree of nonlinearity. Furthermore, the optimal characteristic time scale of the reconstructed modes is also found. The technique is applied to monthly sea surface temperature (SST) time series having a duration of 33 years and covering the globe. Three dominant nonlinear modes were extracted from the time series: the first efficiently separates the annual cycle, the second is responsible for ENSO variability, and combinations of the second and the third modes explain substantial parts of Pacific and Atlantic dynamics. A relation of the obtained modes to decadal natural climate variability including current hiatus in global warming is exhibited and discussed.

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Principal nonlinear dynamical modes of climate variability.

The present paper is the second part of a two-part study on empirical modeling and prediction of climate variability. This paper deals with spatially distributed data, as opposed to the univariate data of Part I. The choice of a basis for effective data compression becomes of the essence. In many applications, it is the set of spatial empirical orthogonal functions that provides the uncorrelated time series of principal components (PCs) used in the learning set. In this paper, the basis of the learning set is obtained instead by applying multichannel singular-spectrum analysis to climatic time series and using the leading spatiotemporal PCs to construct a reduced stochastic model. The effectiveness of this approach is illustrated by predicting the behavior of the Jin–Neelin–Ghil (JNG) hybrid seasonally forced coupled ocean–atmosphere model of El Nino–Southern Oscillation. The JNG model produces spatially distributed and weakly nonstationary time series to which the model reduction and prediction m...

/pdf/predicting-critical-transitions-in-enso-models-part-ii-3wuwvvjs04.pdf

Predicting Critical Transitions in ENSO models. Part II: Spatially Dependent Models

An alternative approach to determining embedding dimension when reconstructing dynamic systems from a noisy time series is proposed. The available techniques of determining embedding dimension (the false nearest-neighbor method, calculation of the correlation integral, and others) are known [H. D. I. Abarbanel, Analysis of Observed Chaotic Data (Springer-Verlag, New York, 1997)] to be inefficient, even at a low noise level. The proposed approach is based on constructing a global model in the form of an artificial neural network. The required amount of neurons and the embedding dimension are chosen so that the description length should be minimal. The considered approach is shown to be appreciably less sensitive to the level and origin of noise, which makes it also a useful tool for determining embedding dimension when constructing stochastic models.

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Dmitry Mukhin

Papers

An Equilibrium Model of the International Price System

Principal nonlinear dynamical modes of climate variability.

Predicting Critical Transitions in ENSO models. Part II: Spatially Dependent Models

Using the minimum description length principle for global reconstruction of dynamic systems from noisy time series

Using the minimum description length principle for global reconstruction of dynamic systems from noisy time series