Khám Phá Quy Trình Bộ Nhớ Dài Trong Dự Đoán Chuỗi Thời Gian Tài Chính Giá Trị Khoảng và Ứng Dụng Của Nó

Li Y, Gault R, and Mcginnity T M, Probabilistic, recurrent, fuzzy neural network for processing noisy time-series data, IEEE Transactions on Neural Networks and Learning Systems, 2021, 33(9): 4851–4860. Brakel J V D, Zhang X C, and Tam S-M, Measuring discontinuities in time series obtained with repeated sample surveys, International Statistical Review, 2021, 88(1): 155–175. Kumar G, Jain S, and Singh U P, Stock market forecasting using computational intelligence: A survey, Archives of Computational Methods in Engineering, 2021, 28(3): 1069–1101. Sezer O B, Gudelek U, and Ozbayoglu M, Financial time series forecasting with deep learning: A systematic literature review: 2005–2019, Applied Soft Computing, 2020, 90: 106181. Lara-Benitez P, Carranza-Garca M, and Riquelme J C, An experimental review on deep learning architectures for time series forecasting, International Journal of Neural Systems, 2021, 31(3): 2130001. Wang Z C, Chen L R, Zhu J M, et al., Double decomposition and optimal combination ensemble learning approach for interval-valued aqi forecasting using streaming data, Environmental Science and Pollution Research, 2020, 27(13): 37802–37817. Wang Z C, Chen L R, Ding Z N, et al., An enhanced interval PM2.5 concentration forecasting model based on bemd and MLP1 with influencing factors, Atmospheric Environment, 2020, 223: 117200.1–117200.16. Maciel L and Ballini R, Functional fuzzy rule-based modeling for interval-valued data: An empirical application for exchange rates forecasting, Computational Economics, 2020, 3: 743–771. Chaiyakan S and Thipwiwatpotjana P, Bounds on mean absolute deviation portfolios under interval-valued expected future asset returns, Computational Management Science, 2021, 8: 195–212. Buansing T, Golan A, and Ullah A, An information-theoretic approach for forecasting interval-valued SP500 daily returns, International Journal of Forecasting, 2020, 36(3): 800–813. Lin W and Gonzalez-Rivera G, Interval-valued time series models: Estimation based on order statistics exploring the agriculture marketing service data, Computational Statistics & Data Analysis, 2016, 100: 694–711. Zhong Y, Zhang Z Z, and Li S M, A constrained interval-valued linear regression model: A new heteroscedasticity estimation method, Journal of Systems Science & Complexity, 2020, 33(6): 2048–2066. Al-Qudaimi A, Ishita approach to construct an interval-valued triangular fuzzy regression model using a novel least-absolute based discrepancy, Engineering Applications of Artificial Intelligence, 2021, 102: 104272. Boukezzoula R and Coquin D, Interval-valued fuzzy regression: Philosophical and methodological issues, Applied Soft Computing, 2021, 103: 107145. Sun Y Y, Ai H, Hong Y M, et al., Threshold autoregressive models for interval-valued time series data, Journal of Econometrics, 2018, 206: 414–446. Maciel L and Ballini R, A fuzzy inference system modeling approach for interval-valued symbolic data forecasting, Knowledge-Based Systems, 2019, 164: 139–149. Maia A L S and Carvalho F, Holt’s exponential smoothing and neural network models for forecasting interval-valued time series, International Journal of Forecasting, 2011, 27(3): 740–759. Hamiye B, Bootstrap based multi-step ahead joint forecast densities for financial interval-valued time series, Communications, 2021, 70(1): 156–179. Guo J, Lu W, Yang J H, et al., A rule-based granular model development for interval-valued time series, International Journal of Approximate Reasoning, 2021, 136: 201–222. Spadon G, Hong S D, Brandoli B, et al., Pay attention to evolution: Time series forecasting with deep graph-evolution learning, IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021, 44(9): 5368–5384. Vishwakarm G K, Paul C, and Elsawah A M, A hybrid feedforward neural network algorithm for detecting outliers in non-stationary multivariate time series, Expert Systems with Applications, 2021, 184: 115545. D’Urso P, Giovanni L, and Massari R, Trimmed fuzzy clustering of financial time series based on dynamic time warping, Annals of Operations Research, 2021, 299(1): 1379–1395. Bisaglia L and Grigoletto M, A new time-varying model for forecasting long-memory series, Statistical Methods & Applications, 2021, 30(1): 139–155. Nath P, Saha P, Middya A I, et al., Long-term time-series pollution forecast using statistical and deep learning methods, Neural Computing and Applications, 2021, 33: 12551–12570. Hurst H E, Long-term storage capacity of reservoirs, American Society of Civil Engineers Tans, 1951, 116: 770–799. Lo A W, Long-term memory in stock market prices, Econometrica, E-Conometric Society, 1991, 59(5): 1279–1313. Geweke J and Porter-Hudak S, The estimation and application of long memory time series models, Journal of Time Series Analysis, 2010, 4(4): 221–238. Gui J, Zheng Z Y, Fu D Z, et al., Long-term correlations and multifractality of toll-free calls in China, Physica A: Statistical Mechanics and Its Applications, 2021, 567(C): 125633. Robinson P M, Log-periodogram regression of time series with long range dependence, Annals of Statistics, 1995, 23(3): 1048–1072. Oral E and Unal G, Modeling and forecasting time series of precious metals: A new approach to multifractal data, Financial Innovation, 2019, 5(1): 28, DOI: https://doi.org/10.1186/s40854-019-0135-3. Granger C W J, Long memory relationships and the aggregation of dynamic models, Journal of Econometrics, 1980, 14(2): 227–238. Hosking J R M, Fractional differencing, Biometrika, 1981, 68(1): 165–176. Gray H L, Zhang N F, and Woodward W A, On generalized fractional processes, Journal of Time Series Analysis, 1989, 10(3): 233–257. Maddanu F, A harmonically weighted filter for cyclical long memory processes, AStA Advances in Statistical Analysis, 2021, 106: 49–78. Kare M, Radoevi D, and Radolovi S, Measuring the macroeconomic impact of economic diplomacy using varfima model for croatia 1990–2018, Economics & Sociology, 2020, 13(3): 230–243. Heyde C C and Gay R, Smoothed periodogram asymptotics and estimation for processes and fields with possible long-range dependence, Stochastic Processes & Their Applications, 1993, 45(1): 169–182. Hosoya Y, The quasi-likelihood approach to statistical inference on multiple time-series with long-range dependence, Journal of Econometrics, 2004, 73(1): 217–236. Nelson D B, Conditional heteroskedasticity in asset returns: A new approach, Modelling Stock Market Volatility, 1991, 59(2): 347–370. Bukhari A H, Raja M, Sulaiman M, et al., Fractional neuro-sequential arfima-lstm for financial market forecasting, IEEE Access, 2020, 8: 71326–71338. Wang J Q, Du Y, and Wang J, LSTM based long-term energy consumption prediction with periodicity, Energy, 2020, 197: 117197. Yuan X, Li L, Wang K, et al., Sampling-interval-aware LSTM for industrial process soft sensing of dynamic time sequences with irregular sampling measurements, IEEE Sensors Journal, 2021, 22(9): 10787–10795. Brockwell P J, Davis R A, et al., Time Series: Theory and Methods, Springer-Verlag, New York, 2015. Ding G Z, Varieties of long memory models, Journal of Econometrics, 1996, 73(1): 61–77. Yang W, Han A, Cai K, et al., ACIX model with interval dummy variables and its application in forecasting interval-valued crude oil prices, Procedia Computer Sciences, 2012, 9: 1273–1282.