32 research outputs found
Day-Ahead Crude Oil Price Forecasting Using a Novel Morphological Component Analysis Based Model
As a typical nonlinear and dynamic system, the crude oil price movement is difficult to predict and its accurate forecasting remains the subject of intense research activity. Recent empirical evidence suggests that the multiscale data characteristics in the price movement are another important stylized fact. The incorporation of mixture of data characteristics in the time scale domain during the modelling process can lead to significant performance improvement. This paper proposes a novel morphological component analysis based hybrid methodology for modeling the multiscale heterogeneous characteristics of the price movement in the crude oil markets. Empirical studies in two representative benchmark crude oil markets reveal the existence of multiscale heterogeneous microdata structure. The significant performance improvement of the proposed algorithm incorporating the heterogeneous data characteristics, against benchmark random walk, ARMA, and SVR models, is also attributed to the innovative methodology proposed to incorporate this important stylized fact during the modelling process. Meanwhile, work in this paper offers additional insights into the heterogeneous market microstructure with economic viable interpretations
Exchange Rate Forecasting Using Entropy Optimized Multivariate Wavelet Denoising Model
Exchange rate is one of the key variables in the international economics and international trade. Its movement constitutes one of the most important dynamic systems, characterized by nonlinear behaviors. It becomes more volatile and sensitive to increasingly diversified influencing factors with higher level of deregulation and global integration worldwide. Facing the increasingly diversified and more integrated market environment, the forecasting model in the exchange markets needs to address the individual and interdependent heterogeneity. In this paper, we propose the heterogeneous market hypothesis- (HMH-) based exchange rate modeling methodology to model the micromarket structure. Then we further propose the entropy optimized wavelet-based forecasting algorithm under the proposed methodology to forecast the exchange rate movement. The multivariate wavelet denoising algorithm is used to separate and extract the underlying data components with distinct features, which are modeled with multivariate time series models of different specifications and parameters. The maximum entropy is introduced to select the best basis and model parameters to construct the most effective forecasting algorithm. Empirical studies in both Chinese and European markets have been conducted to confirm the significant performance improvement when the proposed model is tested against the benchmark models
Crossing the Aisle: Unveiling Partisan and Counter-Partisan Events in News Reporting
News media is expected to uphold unbiased reporting. Yet they may still
affect public opinion by selectively including or omitting events that support
or contradict their ideological positions. Prior work in NLP has only studied
media bias via linguistic style and word usage. In this paper, we study to
which degree media balances news reporting and affects consumers through event
inclusion or omission. We first introduce the task of detecting both partisan
and counter-partisan events: events that support or oppose the author's
political ideology. To conduct our study, we annotate a high-quality dataset,
PAC, containing 8,511 (counter-)partisan event annotations in 304 news articles
from ideologically diverse media outlets. We benchmark PAC to highlight the
challenges of this task. Our findings highlight both the ways in which the news
subtly shapes opinion and the need for large language models that better
understand events within a broader context. Our dataset can be found at
https://github.com/launchnlp/Partisan-Event-Dataset.Comment: EMNLP'23 Finding
All Things Considered: Detecting Partisan Events from News Media with Cross-Article Comparison
Public opinion is shaped by the information news media provide, and that
information in turn may be shaped by the ideological preferences of media
outlets. But while much attention has been devoted to media bias via overt
ideological language or topic selection, a more unobtrusive way in which the
media shape opinion is via the strategic inclusion or omission of partisan
events that may support one side or the other. We develop a latent
variable-based framework to predict the ideology of news articles by comparing
multiple articles on the same story and identifying partisan events whose
inclusion or omission reveals ideology. Our experiments first validate the
existence of partisan event selection, and then show that article alignment and
cross-document comparison detect partisan events and article ideology better
than competitive baselines. Our results reveal the high-level form of media
bias, which is present even among mainstream media with strong norms of
objectivity and nonpartisanship. Our codebase and dataset are available at
https://github.com/launchnlp/ATC.Comment: EMNLP'23 Main Conferenc
Forecasting Crude Oil Price with Multiscale Denoising Ensemble Model
Crude oil price becomes more volatile and sensitive to increasingly diversified influencing factors with higher level of deregulations worldwide. Current methodologies are being challenged as they have been constrained by traditional approaches assuming homogeneous time horizons and investment strategies. Approximations they provided over the long term time horizon no longer satisfy the accuracy requirement at shorter term and more microlevels. This paper proposes a novel crude oil price forecasting model based on the wavelet denoising ARMA models ensemble by least square support vector regression with the reduced forecasting matrix dimensions by independent component analysis. The proposed methodology combines the multi resolution analysis and nonlinear ensemble framework. The wavelet denoising based algorithm is introduced to separate and extract the underlying data components with distinct features, corresponding to investors with different investment scales, which are modeled with time series models of different specifications and parameters. Then least square support vector regression is introduced to nonlinearly ensemble results based on different wavelet families to further reduce the estimation biases and improve the forecasting generalizability. Empirical studies show the significant performance improvement when the proposed model is tested against the bench-mark models
Controllable nonlinear propagation of partially incoherent Airy beams
The self-accelerating beams such as the Airy beam show great potentials in
many applications including optical manipulation, imaging and communication.
However, their superior features during linear propagation could be easily
corrupted by optical nonlinearity or spatial incoherence individually. Here we
investigate how the interaction of spatial incoherence and nonlinear
propagation affect the beam quality of Airy beam, and find that the two
destroying factors can in fact balance each other. Our results show that the
influence of coherence and nonlinearity on the propagation of PIABs can be
formulated as two exponential functions that have factors of opposite signs.
With appropriate spatial coherence length, the PIABs not only resist the
corruption of beam profile caused by self-focusing nonlinearity, but also
exhibits less anomalous diffraction caused by the self-defocusing nonlinearity.
Our work provides deep insight into how to maintain the beam quality of
self-accelerating Airy beams by exploiting the interaction between partially
incoherence and optical nonlinearity. Our results may bring about new
possibilities for optimizing partially incoherent structured field and
developing related applications such as optical communication, incoherent
imaging and optical manipulations.Comment: 11pages,6 figure
Caustic analysis of partially coherent self-accelerating beams: Investigating self-healing property
We employed caustic theory to analyze the propagation dynamics of partially
coherent self-accelerating beams such as self-healing of partially coherent
Airy beams. Our findings revealed that as the spatial coherence decreases, the
self-healing ability of beams increases. This result have been demonstrated
both in simulation and experiment. This is an innovative application of the
caustic theory to the field of partially coherent structured beams, and
provides a comprehensive understanding of self-healing property. Our results
have significant implications for practical applications of partially coherent
beams in fields such as optical communication, encryption, and imaging.Comment: 9 pages, 4 figure
Forecasting Crude Oil Risk Using a Multivariate Multiscale Convolutional Neural Network Model
In light of the increasing level of correlation and dependence between the crude oil markets and the external influencing factors in the related financial markets, we propose a new multivariate empirical decomposition convolutional neural network model to incorporate the external influence of financial markets such as stock market and exchange market in a multiscale setting into the modeling of crude oil market risk movement. We propose a multivariate empirical model decomposition to analyze the finer details of interdependence among risk movement of different markets across different time horizons or scales. We also introduce the convolutional neural network to construct a new nonlinear ensemble algorithm to reduce the estimation bias and improve the forecasting accuracy. We used the major crude oil price data, stock market index, and the euro/United States dollar exchange rate data to evaluate the performance of the multivariate empirical model decomposition convolutional neural network model. The combination of both the multivariate empirical model decomposition and the convolutional neural network model in this paper has produced the risk forecasts with significantly improved risk forecasting accuracy
Key Points of Simple Cultivation Technique for Whole-plant Silage Maize in Guangxi
With the vigorous development of animal husbandry in Guangxi, feed problems have become increasingly prominent. Silage maize has the characteristics of rapid growth, high nutritional value, easy digestion and absorption, and a large amount of biological output being obtained in a short time. It is one of the ideal basic feeds for cattle and sheep and other breeding industries. Based on this, the simple cultivation technique of whole-plant silage maize was summarized from the aspects of land preparation, selection of maize variety, sowing, field management, pest control and timely harvesting, so as to provide technical reference for scientific planting of silage maize in Guangxi
Wavelet Entropy Based Analysis and Forecasting of Crude Oil Price Dynamics
For the modeling of complex and nonlinear crude oil price dynamics and movement, wavelet analysis can decompose the time series and produce multiple economically meaningful decomposition structures based on different assumptions of wavelet families and decomposition scale. However, the determination of the optimal model specification will critically affect the forecasting accuracy. In this paper, we propose a new wavelet entropy based approach to identify the optimal model specification and construct the effective wavelet entropy based forecasting models. The wavelet entropy algorithm is introduced to determine the optimal wavelet families and decomposition scale, that will produce the improved forecasting performance. Empirical studies conducted in the crude oil markets show that the proposed algorithm outperforms the benchmark model, in terms of conventional performance evaluation criteria for the model forecasting accuracy