157,771 research outputs found
Dynamic dependence networks: Financial time series forecasting and portfolio decisions (with discussion)
We discuss Bayesian forecasting of increasingly high-dimensional time series,
a key area of application of stochastic dynamic models in the financial
industry and allied areas of business. Novel state-space models characterizing
sparse patterns of dependence among multiple time series extend existing
multivariate volatility models to enable scaling to higher numbers of
individual time series. The theory of these "dynamic dependence network" models
shows how the individual series can be "decoupled" for sequential analysis, and
then "recoupled" for applied forecasting and decision analysis. Decoupling
allows fast, efficient analysis of each of the series in individual univariate
models that are linked-- for later recoupling-- through a theoretical
multivariate volatility structure defined by a sparse underlying graphical
model. Computational advances are especially significant in connection with
model uncertainty about the sparsity patterns among series that define this
graphical model; Bayesian model averaging using discounting of historical
information builds substantially on this computational advance. An extensive,
detailed case study showcases the use of these models, and the improvements in
forecasting and financial portfolio investment decisions that are achievable.
Using a long series of daily international currency, stock indices and
commodity prices, the case study includes evaluations of multi-day forecasts
and Bayesian portfolio analysis with a variety of practical utility functions,
as well as comparisons against commodity trading advisor benchmarks.Comment: 31 pages, 9 figures, 3 table
Differential Evolution Algorithm based Hyper-Parameters Selection of Transformer Neural Network Model for Load Forecasting
Accurate load forecasting plays a vital role in numerous sectors, but
accurately capturing the complex dynamics of dynamic power systems remains a
challenge for traditional statistical models. For these reasons, time-series
models (ARIMA) and deep-learning models (ANN, LSTM, GRU, etc.) are commonly
deployed and often experience higher success. In this paper, we analyze the
efficacy of the recently developed Transformer-based Neural Network model in
Load forecasting. Transformer models have the potential to improve Load
forecasting because of their ability to learn long-range dependencies derived
from their Attention Mechanism. We apply several metaheuristics namely
Differential Evolution to find the optimal hyperparameters of the
Transformer-based Neural Network to produce accurate forecasts. Differential
Evolution provides scalable, robust, global solutions to non-differentiable,
multi-objective, or constrained optimization problems. Our work compares the
proposed Transformer based Neural Network model integrated with different
metaheuristic algorithms by their performance in Load forecasting based on
numerical metrics such as Mean Squared Error (MSE) and Mean Absolute Percentage
Error (MAPE). Our findings demonstrate the potential of metaheuristic-enhanced
Transformer-based Neural Network models in Load forecasting accuracy and
provide optimal hyperparameters for each model.Comment: 6 Pages, 6 Figures, 2 Table
Towards Developing a Travel Time Forecasting Model for Location-Based Services: a Review
Travel time forecasting models have been studied intensively as a subject of Intelligent Transportation Systems (ITS), particularly in the topics of advanced traffic management systems (ATMS), advanced traveler information systems (ATIS), and commercial vehicle operations (CVO). While the concept of travel time forecasting is relatively simple, it involves a notably complicated task of implementing even a simple model. Thus, existing forecasting models are diverse in their original formulations, including mathematical optimizations, computer simulations, statistics, and artificial intelligence. A comprehensive literature review, therefore, would assist in formulating a more reliable travel time forecasting model. On the other hand, geographic information systems (GIS) technologies primarily provide the capability of spatial and network database management, as well as technology management. Thus, GIS could support travel time forecasting in various ways by providing useful functions to both the managers in transportation management and information centers (TMICs) and the external users. Thus, in developing a travel time forecasting model, GIS could play important roles in the management of real-time and historical traffic data, the integration of multiple subsystems, and the assistance of information management. The purpose of this paper is to review various models and technologies that have been used for developing a travel time forecasting model with geographic information systems (GIS) technologies. Reviewed forecasting models in this paper include historical profile approaches, time series models, nonparametric regression models, traffic simulations, dynamic traffic assignment models, and neural networks. The potential roles and functions of GIS in travel time forecasting are also discussed.
Linear models, smooth transition autoregressions and neural networks for forecasting macroeconomic time series: A reexamination
In this paper we examine the forecast accuracy of linear autoregressive, smooth transition autoregressive (STAR), and neural network (NN) time series models for 47 monthly macroeconomic variables of the G7 economies. Unlike previous studies that typically consider multiple but fixed model specifications, we use a single but dynamic specification for each model class. The point forecast results indicate that the STAR model generally outperforms linear autoregressive models. It also improves upon several fixed STAR models, demonstrating that careful specification of nonlinear time series models is of crucial importance. The results for neural network models are mixed in the sense that at long forecast horizons, an NN model obtained using Bayesian regularization produces more accurate forecasts than a corresponding model specified using the specific-to-general approach. Reasons for this outcome are discussed.forecast combination; forecast evaluation; neural network model; nonlinear modelling; nonlinear forecasting JEL Codes: C22; C53
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