5,490 research outputs found
Time Series is a Special Sequence: Forecasting with Sample Convolution and Interaction
Time series is a special type of sequence data, a set of observations
collected at even time intervals and ordered chronologically. Existing deep
learning techniques use generic sequence models (e.g., recurrent neural
network, Transformer model, or temporal convolutional network) for time series
analysis, which ignore some of its unique properties. In particular, three
components characterize time series: trend, seasonality, and irregular
components, and the former two components enable us to perform forecasting with
reasonable accuracy. Other types of sequence data do not have such
characteristics. Motivated by the above, in this paper, we propose a novel
neural network architecture that conducts sample convolution and interaction
for temporal modeling and apply it for the time series forecasting problem,
namely \textbf{SCINet}. Compared to conventional dilated causal convolution
architectures, the proposed downsample-convolve-interact architecture enables
multi-resolution analysis besides expanding the receptive field of the
convolution operation, which facilitates extracting temporal relation features
with enhanced predictability. Experimental results show that SCINet achieves
significant prediction accuracy improvement over existing solutions across
various real-world time series forecasting datasets
Decomposition by Causal Forces: A Procedure for Forecasting Complex Time Series
Causal forces are a way of summarizing forecasters expectations about what will happen to a time series in the future. Contrary to the common assumption for extrapolation, time series are not always subject to consistent forces that point in the same direction. Some are affected by conflicting causal forces; we refer to these as complex times series. It would seem that forecasting these times series would be easier if one could decompose the series to eliminate the effects of the conflicts. Given forecasts subject to high uncertainty, we hypothesized that a time series could be effectively decomposed under two conditions: 1) if domain knowledge can be used to structure the problem so that causal forces are consistent for two or more component series, and 2) when it is possible to obtain relatively accurate forecasts for each component. Forecast accuracy for the components can be assessed by testing how well they can be forecast on early hold-out data. When such data are not available, historical variability may be an adequate substitute. We tested decomposition by causal forces on 12 complex annual time series for automobile accidents, airline accidents, personal computer sales, airline revenues, and cigarette production. The length of these series ranged from 16 years for airline revenues to 56 years for highway safety data. We made forecasts for one to ten horizons, obtaining 800 forecasts through successive updating. For nine series in which the conditions were completely or partially met, the forecast error (MdAPE) was reduced by more than half. For three series in which the conditions were not met, decomposition by causal forces had little effect on accuracy.airline accidents, extrapolation, Holt s exponential smoothing, model formulation, personal computers, revenue forecasting, transportation safety.
A novel ensemble method for electric vehicle power consumption forecasting: Application to the Spanish system
The use of electric vehicle across the world has become one of the most challenging issues for environmental policies. The galloping climate change and the expected running out of fossil fuels turns the use of such non-polluting cars into a priority for most developed countries. However, such a use has led to major concerns to power companies, since they must adapt their generation to a new scenario, in which electric vehicles will dramatically modify the curve of generation. In this paper, a novel approach based on ensemble learning is proposed. In particular, ARIMA, GARCH and PSF algorithms' performances are used to forecast the electric vehicle power consumption in Spain. It is worth noting that the studied time series of consumption is non-stationary and adds difficulties to the forecasting process. Thus, an ensemble is proposed by dynamically weighting all algorithms over time. The proposal presented has been implemented for a real case, in particular, at the Spanish Control Centre for the Electric Vehicle. The performance of the approach is assessed by means of WAPE, showing robust and promising results for this research field.Ministerio de Economía y Competitividad Proyectos ENE2016-77650-R, PCIN-2015-04 y TIN2017-88209-C2-R
Disentangling Structured Components: Towards Adaptive, Interpretable and Scalable Time Series Forecasting
Multivariate time-series (MTS) forecasting is a paramount and fundamental
problem in many real-world applications. The core issue in MTS forecasting is
how to effectively model complex spatial-temporal patterns. In this paper, we
develop a adaptive, interpretable and scalable forecasting framework, which
seeks to individually model each component of the spatial-temporal patterns. We
name this framework SCNN, as an acronym of Structured Component-based Neural
Network. SCNN works with a pre-defined generative process of MTS, which
arithmetically characterizes the latent structure of the spatial-temporal
patterns. In line with its reverse process, SCNN decouples MTS data into
structured and heterogeneous components and then respectively extrapolates the
evolution of these components, the dynamics of which are more traceable and
predictable than the original MTS. Extensive experiments are conducted to
demonstrate that SCNN can achieve superior performance over state-of-the-art
models on three real-world datasets. Additionally, we examine SCNN with
different configurations and perform in-depth analyses of the properties of
SCNN
Symbolic representation of scenarios in Bologna airport on virtual reality concept
This paper is a part of a big Project named Retina Project, which is focused in reduce the workload of an ATCO. It uses the last technological advances as Virtual Reality concept. The work has consisted in studying the different awareness situations that happens daily in Bologna Airport. It has been analysed one scenario with good visibility where the sun predominates and two other scenarios with poor visibility where the rain and the fog dominate. Due to the study of visibility in the three scenarios computed, the conclusion obtained is that the overlay must be shown with a constant dimension regardless the position of the aircraft to be readable by the ATC and also, the frame and the flight strip should be coloured in a showy colour (like red) for a better control by the ATCO
Management by Trajectory: Trajectory Management Study Report
In order to realize the full potential of the Next Generation Air Transportation System (NextGen), improved management along planned trajectories between air navigation service providers (ANSPs) and system users (e.g., pilots and airline dispatchers) is needed. Future automation improvements and increased data communications between aircraft and ground automation would make the concept of Management by Trajectory (MBT) possible
Complexity challenges in ATM
After more than 4 years of activity, the ComplexWorld Network, together with the projects and PhDs covered under the SESAR long-term research umbrella, have developed sound research material contributing to progress beyond the state
of the art in fields such as resilience, uncertainty, multi-agent systems, metrics and data science. The achievements made by the ComplexWorld stakeholders have also led to the identification of new challenges that need to be addressed in the future. In order to pave the way for complexity science research in Air Traffic Management (ATM) in the coming years, ComplexWorld requested external assessments on how the challenges have been covered and where there are existing gaps. For that purpose, ComplexWorld, with the support of EUROCONTROL, established an expert panel to review selected documentation developed by the network and provide their assessment on their topic of expertise
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