Abstract:In this work we propose a new hybrid model, a combination of the manifold learning Principal Components (PC) technique and the traditional multiple regression (PC-regression), for short and medium-term forecasting of daily, aggregated, day-ahead, electricity system-wide load in the Greek Electricity Market for the period 2004-2014. PC-regression is shown to effectively capture the intraday, intraweek and annual patterns of load. We compare our model with a number of classical statistical approaches (Holt-Winters exponential smoothing of its generalizations Error-Trend-Seasonal, ETS models, the Seasonal Autoregressive Moving Average with exogenous variables, Seasonal Autoregressive Integrated Moving Average with eXogenous (SARIMAX) model as well as with the more sophisticated artificial intelligence models, Artificial Neural Networks (ANN) and Support Vector Machines (SVM). Using a number of criteria for measuring the quality of the generated in-and out-of-sample forecasts, we have concluded that the forecasts of our hybrid model outperforms the ones generated by the other model, with the SARMAX model being the next best performing approach, giving comparable results. Our approach contributes to studies aimed at providing more accurate and reliable load forecasting, prerequisites for an efficient management of modern power systems.
The Efficient Market Hypothesis (EMH) is widely accepted to hold true under
certain assumptions. One of its implications is that the prediction of stock
prices at least in the short run cannot outperform the random walk model. Yet,
recently many studies stressing the psychological and social dimension of
financial behavior have challenged the validity of the EMH. Towards this aim,
over the last few years, internet-based communication platforms and search
engines have been used to extract early indicators of social and economic
trends. Here, we used Twitter's social networking platform to model and
forecast the EUR/USD exchange rate in a high-frequency intradaily trading
scale. Using time series and trading simulations analysis, we provide some
evidence that the information provided in social microblogging platforms such
as Twitter can in certain cases enhance the forecasting efficiency regarding
the very short (intradaily) forex.Comment: This is a prior version of the paper published at NETNOMICS. The
final publication is available at
http://www.springer.com/economics/economic+theory/journal/1106
In this paper a multiple-input multiple-output (MIMO) simulation model for vehicle-to-vehicle (V2V) communication channels in an urban cross-junction scenario is presented. The model is an extension and modification of an existing Tjunction model in the literature. Four propagation processes are considered: line-of-sight (LOS), single bounce reflections from side walls, double bounce reflections from side walls, and single bounce reflections from the corner of the building in front of the transmitter (TX) and receiver (RX). Each propagation process is linked to a cluster of scatterers, with a cluster size that varies with respect to the position of the TX and RX. The relations between angle-of-arrivals and angle-of-departures of all Multipath Components (MPCs) are derived depending on the different positions of the TX and RX. For the single bounce reflections from the corner a new method is being used where the scatterers are distributed randomly in a triangular plane, based on the assumption that corners of buildings typically have different scattering objects contributing to the reflection process. A complete expression for the time-variant transfer function is then derived by super positioning all contributions, including the LOS when this is available. The final results show that our model follows a realistic measurement based path-loss model, which subsequently makes the model a suitable candidate for analyzing MIMO V2V fading channels in cross-junction scattering environments.
Achieving sustainable urban mobility is a complex and multivariate issue that requires constant monitoring and evaluation of the existing situation and possible reconsideration and adjustment of objectives and strategy. The use of indicators is perhaps the most common methodological assessment tool for the sustainable urban mobility level achieved. Key performance indicators can provide in a simple way useful information for complex phenomena in an urban area (i.e. identification of the specific problems and their development over time). Thus, they contribute at a great degree to the decisions made concerning the prioritization of measures and policies toward achieving a goal. However, the use of indicators often constitutes a highly time consuming and costly process due to the large volumes of raw data required for their calculation. In recent years, a solution toward this problem is attempted to be given through the adoption of new technologies and approaches, such as the collection and export of 'big data' from social networks such as Facebook, Twitter, etc. Social networks provide to their users a continuous and enhanced ability for communication, interface and interaction. Such networks are therefore an important potential tool for the promotion of research in the transport sector, as the amount of data generated in their context gives the possibility to analyse and investigate with greater precision critical issues (e.g. trips characteristics) of urban mobility. The present study is an attempt to link the indicators related to sustainable mobility with social networks. The main advantage resulting from the above link, beyond the possibility of a more precise evaluation of the indicators, is to highlight the society's position toward the prioritization of the various transport-related aspects and measures.
We address a three-tier numerical framework based on nonlinear manifold learning for the forecasting of high-dimensional time series, relaxing the “curse of dimensionality” related to the training phase of surrogate/machine learning models. At the first step, we embed the high-dimensional time series into a reduced low-dimensional space using nonlinear manifold learning (local linear embedding and parsimonious diffusion maps). Then, we construct reduced-order surrogate models on the manifold (here, for our illustrations, we used multivariate autoregressive and Gaussian process regression models) to forecast the embedded dynamics. Finally, we solve the pre-image problem, thus lifting the embedded time series back to the original high-dimensional space using radial basis function interpolation and geometric harmonics. The proposed numerical data-driven scheme can also be applied as a reduced-order model procedure for the numerical solution/propagation of the (transient) dynamics of partial differential equations (PDEs). We assess the performance of the proposed scheme via three different families of problems: (a) the forecasting of synthetic time series generated by three simplistic linear and weakly nonlinear stochastic models resembling electroencephalography signals, (b) the prediction/propagation of the solution profiles of a linear parabolic PDE and the Brusselator model (a set of two nonlinear parabolic PDEs), and (c) the forecasting of a real-world data set containing daily time series of ten key foreign exchange rates spanning the time period 3 September 2001–29 October 2020.
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