A Hybrid Prediction Method for Stock Price Using LSTM and Ensemble EMD by Yang Yujun, Yang Yimei, Xiao Jianhua

“…We use comprehensive EMD to decompose the complex original stock price time series into several subsequences which are smoother, more regular and stable than the original time series. …”
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Analysis of Streamflow Changes under Climate Change Using Rainfall-Runoff Model in the Kor River Basin by zahra hejazizadeh, parisa sadat ashofteh, ebrahim fatahi, zahra Gholampour

“…Then, the downscaled monthly temperature and rainfall time series in next period were employed to rainfall-runoff model and the monthly runoff time series were simulated for period ۲۰۱۱- ۲۰۴۰. …”
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Chaotic Vibration Prediction of a Free-Floating Flexible Redundant Space Manipulator by Congqing Wang, Linfeng Wu

“…The one-step prediction model for the chaotic time series and the chaotic vibration was established by using support vector regression (SVR) prediction model with RBF kernel function. …”
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Study on the Precursors of Coal-Rock Fracture Based on the Maximum Lyapunov Exponent of Acoustic Emission Parameters by Guoqiang Cheng, Xiaohui Sun, Jiangong Li

“…PFC is used to simulate the coal-rock uniaxial compression test for extracting the time series of AE event number, calculate the maximum Lyapunov exponent of the time series, and analyze the chaotic characteristics of AE event number. …”
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Approach to Predict Land Subsidence in Old Goafs considering the Influence of Engineering Noise by Chao Du, Fajin Zu, Chunpeng Han

“…In order to reduce the influence of engineering noise on the accuracy of subsidence prediction, it is proposed to use the Daubechies (DB) wavelet to decompose the original subsidence time series; the items with the low-frequency trend, after decomposition, are predicted using long short-term memory (LSTM) model, items with high-frequency noise used the autoregressive (AR) time series model to make predictions, and the prediction results of the low-frequency trend term and the high-frequency noise term are summed to obtain the total time series predicted value. …”
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The effect of climate change on the precipitable water content in the north coast of the Persian Gulf by Tayebeh Dehghani, mohammad saligheh, Bohloul Alijani

“…The NCEP / NCAR base-station data with an arc-value of 0.125 was used to analyze the past and present precipitable water patterns and to reveal the process of this time series. Time series analysis of precipitable water was performed using two SENS tilt estimators and Man-Kendall test. …”
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Identifying and Evaluating Chaotic Behavior in Hydro-Meteorological Processes by Soojun Kim, Yonsoo Kim, Jongso Lee, Hung Soo Kim

“…The time series in the period of approximately one year were extracted from the original series using the wavelet transform. …”
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A Fast and Efficient Markov Chain Monte Carlo Method for Market Microstructure Model by Sun Yapeng, Peng Hui, Xie Wenbiao

“…The non-linear market microstructure (MM) model for financial time series modeling is a flexible stochastic volatility model with demand surplus and market liquidity. …”
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Fault Diagnosis Method for Rotating Machinery Based on Hierarchical Amplitude-Aware Permutation Entropy and Pairwise Feature Proximity by Ling Shu, Jinxing Shen, Xiaoming Liu

“…With a view to solving the defect that multiscale amplitude-aware permutation entropy (MAAPE) can only quantify the low-frequency features of time series and ignore the high-frequency features which are equally important, a novel nonlinear time series feature extraction method, hierarchical amplitude-aware permutation entropy (HAAPE), is proposed. …”
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The effects of meteorological drought on oak forest dieback in Ilam province by shamsallah Asgari, tayeb razi, Mohamadreza Jafari, Ali Akbar noroozi

“…By combining the oak forest drying layer with the output layers derived from drought zoning, visual indicators were created, and statistical analysis was conducted for three 5-year time series. The results demonstrate a correlation coefficient of 96.6% and an explanation coefficient of R2 = 0.985 for the 2002-2006 time series, a correlation coefficient of 95.4% and an explanation coefficient of R2 = 0.980 for the 2007-2011 time series, and a correlation coefficient of 98.8% and an explanation coefficient of R2 = 0.995 for the 2012-2016 time series. …”
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Contraintes climatiques dans les Préalpes françaises : évolution récente et conséquences potentielles futures by Sylvain Bigot, Sandra Rome

“…The climate of the French Alps remains poorly described, and its interannual variability is difficult to study because of the wide variety of local topoclimatic characteristics and the lack of available long-term time-series. Regional consequences by certain climatic threat on various economic activities are nevertheless expected. …”
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Analysis in Economics: Peer to Peer Lending as an Inflation Indicator by Syakirah Putri Septian, Akbar Almuttaqin

“…The method used is vector autoregressive which can explain the two-way relationship of two variables and seasonal aspects in time series data. The results of this study are that the two variables influence each other and there is an influence of the time aspect in the time series data. …”
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Volatility Similarity and Spillover Effects in G20 Stock Market Comovements: An ICA-Based ARMA-APARCH-M Approach by Shanglei Chai, Zhen Zhang, Mo Du, Lei Jiang

“…The evidence has shown that the ICA method can more accurately capture market comovements with nonnormal distributions of the financial time series data by transforming the multivariate time series into statistically independent components (ICs). …”
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Further Analysis of Non-Gaussian Wind Pressure Peak Factor Calculation by Tao Ye, Ledong Zhu, Zhongxu Tan, Lanlan Li

“…Based on the basic theory of fractal, the R/S analysis of wind pressure time series is made, and the fractal characteristics of wind pressure coefficient time series are explained. …”
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Study of air pollution of Isfahan based on NO2 measured data by OMI Satellite sensor by Atefeh Shahmohammadi, Ali Bayat, Saeed mashhadizadeh Maleki

“…These statistically meaningful peaks corresponding periods have been eliminated from the mean monthly nitrogen dioxide time series, and with the linear fit on the residual time series, the trend has been calculated. …”
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Investigation of the Implications of “Haze Special Law” on Air Quality in South Korea by Jian Wang, Junseok Kim, Wei Shao

“…Hurst, Renyi, and Holder exponents were used to analyze the characteristics of the concentration time series of PM2.5 and NO2. The results showed that multifractality exists in each season interval and the multifractal degree of PM2.5 is stronger than that of NO2. …”
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A Novel First-Order Fuzzy Rules-Based Forecasting System Using Distance Measures Approach for Financial Market Forecasting by Shahbaz Gul Hassan, Tran Thi Kieuvan, Shuangyin Liu, Harish Garg, Munawar Hassan, Shafqat Iqbal

“…As an illustration, this study presented an analysis of a new fuzzy time-series (FTS) approach and comparison with traditional forecasting models for prediction of gram pulse production. …”
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Calibration of Low-cost Gas Sensors for Air Quality Monitoring by Dimitris Margaritis, Christos Keramydas, Ioannis Papachristos, Dimitra Lambropoulou

“…The random forest algorithm appeared to have an advantage in several cases, mostly in terms of following the pattern in the O3 and SO2 time series, but also in terms of the average error and bias for all pollutants.…”
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Dynamical Variety of Shapes in Financial Multifractality by Stanisław Drożdż, Rafał Kowalski, Paweł Oświȩcimka, Rafał Rak, Robert Gȩbarowski

“…The concept of multifractality offers a powerful formal tool to filter out a multitude of the most relevant characteristics of complex time series. The related studies thus far presented in the scientific literature typically limit themselves to evaluation of whether a time series is multifractal, and width of the resulting singularity spectrum is considered a measure of the degree of complexity involved. …”
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Characteristics of Spring Phenological Changes in China over the Past 50 Years by Junhu Dai, Huanjiong Wang, Quansheng Ge

“…Since most phenological time series are discontinuous because of observation interruptions at certain period, we first interpolated phenological time series by using the optimal model between the spring warming (SW) model and the UniChill model to form continuous time series. …”
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