48,362 research outputs found
Autoregressive Neuronale Netze - Univariate, Multivariate und Kointegrierte Modelle mit einer Anwendung aus dem Bereich der deutschen Automobilindustrie
Prediction of economic variables is a basic component not only for economic models, but also for many business decisions. Nevertheless it is difficult to produce accurate predictions in times of economic crises, which cause nonlinear effects in the data. In this dissertation a nonlinear model for analysis of time series with nonlinear effects is introduced. Linear autoregressive processes are extended by neural networks to overcome the problem of nonlinearity. This idea is based on the universal approximation property of single hidden layer feedforward neural networks of Hornik (1993). Univariate Autoregressive Neural Network Processes (AR-NN) as well as Vector Autoregressive Neural Network Processes (VAR-NN) and Neural Network Vector Error Correction Models (NN-VEC) are introduced. Various methods for variable selection, parameter estimation and inference are discussed. AR-NN's as well as a NN-VEC are used for prediction and analysis of the relationships between 4 variables related to the German automobile industry: The US Dollar to Euro exchange rate, the industrial output of the German automobile industry, the sales of imported cars in the USA and an index of shares of German automobile manufacturing companies. Prediction results are compared to various linear and nonlinear univariate and multivariate models
Neural Network Ensembles for Time Series Prediction
Rapidly evolving businesses generate massive
amounts of time-stamped data sequences and defy a demand
for massively multivariate time series analysis. For such data
the predictive engine shifts from the historical auto-regression
to modelling complex non-linear relationships between multidimensional
features and the time series outputs. In order to
exploit these time-disparate relationships for the improved time
series forecasting, the system requires a flexible methodology
of combining multiple prediction models applied to multiple
versions of the temporal data under significant noise component
and variable temporal depth of predictions. In reply
to this challenge a composite time series prediction model
is proposed which combines the strength of multiple neural
network (NN) regressors applied to the temporally varied
feature subsets and the postprocessing smoothing of outputs
developed to further reduce noise. The key strength of the model
is its excellent adaptability and generalisation ability achieved
through a highly diversified set of complementary NN models.
The model has been evaluated within NISIS Competition 2006
and NN3 Competition 2007 concerning prediction of univariate
and multivariate time-series. It showed the best predictive
performance among 12 competitive models in the NISIS 2006
and is under evaluation within NN3 2007 Competition
Neural Networks with Non-Uniform Embedding and Explicit Validation Phase to Assess Granger Causality
A challenging problem when studying a dynamical system is to find the
interdependencies among its individual components. Several algorithms have been
proposed to detect directed dynamical influences between time series. Two of
the most used approaches are a model-free one (transfer entropy) and a
model-based one (Granger causality). Several pitfalls are related to the
presence or absence of assumptions in modeling the relevant features of the
data. We tried to overcome those pitfalls using a neural network approach in
which a model is built without any a priori assumptions. In this sense this
method can be seen as a bridge between model-free and model-based approaches.
The experiments performed will show that the method presented in this work can
detect the correct dynamical information flows occurring in a system of time
series. Additionally we adopt a non-uniform embedding framework according to
which only the past states that actually help the prediction are entered into
the model, improving the prediction and avoiding the risk of overfitting. This
method also leads to a further improvement with respect to traditional Granger
causality approaches when redundant variables (i.e. variables sharing the same
information about the future of the system) are involved. Neural networks are
also able to recognize dynamics in data sets completely different from the ones
used during the training phase
Data-driven Soft Sensors in the Process Industry
In the last two decades Soft Sensors established themselves as a valuable alternative to the traditional means for the acquisition of critical process variables, process monitoring and other tasks which are related to process control. This paper discusses characteristics of the process industry data which are critical for the development of data-driven Soft Sensors. These characteristics are common to a large number of process industry fields, like the chemical industry, bioprocess industry, steel industry, etc. The focus of this work is put on the data-driven Soft Sensors because of their growing popularity, already demonstrated usefulness and huge, though yet not completely realised, potential. A comprehensive selection of case studies covering the three most important Soft Sensor application fields, a general introduction to the most popular Soft Sensor modelling techniques as well as a discussion of some open issues in the Soft Sensor development and maintenance and their possible solutions are the main contributions of this work
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