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Benchmarking XGBoost for Financial Time Series Forecasting [PDF]
SHS Web of ConferencesIt is difficult to accurately predict the direction of asset price changes, which is a key problem in financial forecasting. At present, traditional time series model can not make up for the shortcomings in the face of non-linear volatility.
Li Jianzhang
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Modeling Similarities Among Multi-Dimensional Financial Time Series
IEEE Access, 2018 Pairs trading is one of the most successful strategies for stock investment. The performance of pairs trading heavily depends on modeling how similarity of two paired financial signals.
Dawei Cheng +3 more
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Forbidden patterns in financial time series [PDF]
Chaos: An Interdisciplinary Journal of Nonlinear Science, 2008 The existence of forbidden patterns, i.e., certain missing sequences in a given time series, is a recently proposed instrument of potential application in the study of time series. Forbidden patterns are related to the permutation entropy, which has the basic properties of classic chaos indicators, such as Lyapunov exponent or Kolmogorov entropy, thus ...
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Testing financial time series for autocorrelation: Robust Tests
Ciencia Ergo Sum, 2020 Se estudian dos estadísticos de Portmanteau modificados bajo supuestos de dependencia comunes en aplicaciones financieras que pueden utilizarse para comprobar que series de tiempo heterocedásticas son serialmente incorreladas sin suponer independencia o ...
Nelson Omar Muriel Torrero
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Frontiers in Time Series and Financial Econometrics: An overview [PDF]
Journal of Econometrics, 2015 Two of the fastest growing frontiers in econometrics and quantitative finance are time series and financial econometrics. Significant theoretical contributions to financial econometrics have been made by experts in statistics, econometrics, mathematics, and time series analysis.
Shiqing Ling +2 more
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A Hybrid BRNN-ARIMA Model for Financial Time Series Forecasting
Sukkur IBA Journal of Computing and Mathematical Sciences, 2022 The accurate forecasting of time series is difficult and for exchange rate more difficult as well. Because it is difficult to predict as they continuously fluctuate during trading hours.
Muhammad Najamuddin, Samreen Fatima
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Tracking the Evolution of Financial Time Series Clusters [PDF]
Proceedings of the 5th Workshop on Data Science for Macro-modeling with Financial and Economic Datasets, 2019 Nowadays, a huge amount of applications exist that natively adopt a data-streaming model to represent highly dynamic phenomena. A challenging application is constituted by data from the stock market, where the stock prices are naturally modeled as data streams that fluctuate very much and remain meaningful only for short amounts of time.
Azzalini D. +3 more
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Financial Time series Forecasting using Holt-Winters in H-step Ahead [PDF]
تحقیقات مالی, 2016 Up to now various methods have been used to predict stock prices and profits. According to financial volatility markets, the most important thing is which of the methods can be applied to predict the optimal decision to help managers and decision makers ...
Hamid Shahriari +2 more
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Modeling Financial Time Series with S-PLUS (2nd Edition)
Journal of Statistical Software, 2007 s not available for ...
Sylvain Sardy, Valerie Chavez-Demoulin
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Financial Forecasting With α-RNNs: A Time Series Modeling Approach
Frontiers in Applied Mathematics and Statistics, 2021 The era of modern financial data modeling seeks machine learning techniques which are suitable for noisy and non-stationary big data. We demonstrate how a general class of exponential smoothed recurrent neural networks (α-RNNs) are well suited to ...
Matthew Dixon +2 more
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