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Mostrando 201 - 220 Resultados de 243 Para Buscar '"The Financial Times"', tiempo de consulta: 0.22s Limitar resultados
  1. 201
    por Henderson, Donna, Lunter, Gerton
    Publicado 2019
    “…Finally we use IOEM to fit two models to a financial time series. A Python implementation is available at https://github.com/luntergroup/IOEM.git.…”
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    Efficient inference in state-space models through adaptive learning in online Monte Carlo expectation maximization
  2. 202
    “…Over the past few years, we have seen an increased need to analyze the dynamically changing behaviors of economic and financial time series. These needs have led to significant demand for methods that denoise non-stationary time series across time and for specific investment horizons (scales) and localized windows (blocks) of time. …”
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    Denoising Non-Stationary Signals via Dynamic Multivariate Complex Wavelet Thresholding
  3. 203
    “…These results confirm that binary projections of financial time series contain significant structural information.…”
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    Mesoscopic Community Structure of Financial Markets Revealed by Price and Sign Fluctuations
  4. 204
    “…Empirical results indicate the validity of the LLFT-SV specification for modelling both “non-standard” financial time series with repeating zero returns, as well as more “typical” data on the S&P 500 and DAX indices. …”
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    A Locally Both Leptokurtic and Fat-Tailed Distribution with Application in a Bayesian Stochastic Volatility Model
  5. 205
    “…This study sheds light on combining stock network analysis and financial time series modeling and highlights that anomalous changes of a stock network can be important criteria for detecting crashes and predicting recoveries of the stock market.…”
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    Crash Diagnosis and Price Rebound Prediction in NYSE Composite Index Based on Visibility Graph and Time-Evolving Stock Correlation Network
  6. 206
    “…Although well accepted by students and staff, different obstacles were encountered; these included limited external funding, scarce own financial, time and personnel resources, and diverging regulations and structures of degree programs at the partnership sites. …”
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    Enhancing an International Perspective in Public Health Teaching through Formalized University Partnerships
  7. 207
    por Jin, Zebin, Jin, Yixiao, Chen, Zhiyun
    Publicado 2022
    “…Combining the nonlinear and non-stationary financial time series can improve prediction accuracy. …”
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    Empirical mode decomposition using deep learning model for financial market forecasting
  8. 208
    “…Shipping indices are extremely volatile, non-stationary, unstructured and non-linear, and more difficult to forecast than other common financial time series. Based on the idea of "decomposition-reconstruction-integration", this article puts forward a combined forecasting model CEEMD-PSO-BiLSTM for shipping index, which overcomes the linearity limitation of traditional models. …”
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    Forecasting shipping index using CEEMD-PSO-BiLSTM model
  9. 209
    por Corazza, Marco, Pizzi, Claudio
    Publicado 2014
    “…As regards the applications, they are related to real problems associated, among the others, to: banks; collateralized fund obligations; credit portfolios; defined benefit pension plans; double-indexed pension annuities; efficient-market hypothesis; exchange markets; financial time series; firms; hedge funds; non-life insurance companies; returns distributions; socially responsible mutual funds; unit-linked contracts. …”
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    Mathematical and statistical methods for actuarial sciences and finance
  10. 210
    “…The model innovatively applies financial technical indicators from financial time series data to wind power time series data and creates a class of model input features that can highly condense the potential relationships between time series data. …”
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    Ultra-short-term wind power prediction method combining financial technology feature engineering and XGBoost algorithm
  11. 211
    por Ghorbani, Mahsa, Chong, Edwin K. P.
    Publicado 2020
    “…To address the time-varying nature of financial time series, we assign exponential weights to the price data so that recent data points are weighted more heavily. …”
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    Stock price prediction using principal components
  12. 212
    “…Modeling was performed by multilayer perceptron–genetic algorithms (MLP–GA) and multilayer perceptron–particle swarm optimization (MLP–PSO) in two scenarios considering Tanh (x) and the default Gaussian function as the output function. The historical financial time series data utilized in this research is from 1996 to 2020, consisting of nine technical indicators. …”
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    Training Multilayer Perceptron with Genetic Algorithms and Particle Swarm Optimization for Modeling Stock Price Index Prediction
  13. 213
    “…Regression models were fit to examine the association of ADL challenges with total caregiver burden and its four domains (emotional, financial, time dependence, and developmental). RESULTS: Parental perceptions of caregiver burden decreased, particularly time dependence and developmental burden, when adult children were less dependent in ADLs, even after adjusting for parental health and behavioral challenges. …”
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    Empirical evaluation of the association between daily living skills of adults with autism and parental caregiver burden
  14. 214
    “…Intraday stock time series are noisier and more complex than other financial time series with longer time horizons, which makes it challenging to predict. …”
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    CEGH: A Hybrid Model Using CEEMD, Entropy, GRU, and History Attention for Intraday Stock Market Forecasting
  15. 215
    por Golestani, Abbas, Gras, Robin
    Publicado 2014
    “…Nonlinear properties of a time series are evaluated and used for long-term predictions. We used financial time series, medical time series and climate time series to evaluate our method. …”
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    Can we predict the unpredictable?
  16. 216
    “…As a concrete example we then study financial time series, showing that a multiplex network analysis can efficiently discriminate crises from periods of financial stability, where standard methods based on time-series symbolization often fail.…”
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    Network structure of multivariate time series
  17. 217
    “…QF-TraderNet composes two neural networks: 1) A long short term memory networks for the feature learning of financial time series; 2) a policy generator network (PGN) for generating the distribution of actions. …”
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    QF-TraderNet: Intraday Trading via Deep Reinforcement With Quantum Price Levels Based Profit-And-Loss Control
  18. 218
    “…A perspective is taken on the intangible complexity of economic and social systems by investigating the dynamical processes producing, storing and transmitting information in financial time series. An extensive analysis based on the moving average cluster entropy approach has evidenced market and horizon dependence in highest-frequency data of real world financial assets. …”
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    Long-Range Dependence in Financial Markets: A Moving Average Cluster Entropy Approach
  19. 219
    “…This format could be used to overcome other barriers to in-person simulation training, including geographic, financial, time, or security.…”
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    Humanitarian Training With Virtual Simulation During a Pandemic
  20. 220
    por Windsor, Edmure, Cao, Wei
    Publicado 2022
    “…The purpose of our study is to demonstrate that it is practicable and effective to incorporate multimodal fusion into financial time series forecasting.…”
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    Improving exchange rate forecasting via a new deep multimodal fusion model
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