A CNN-LSTM-Based Model to Forecast Stock Prices by Wenjie Lu, Jiazheng Li, Yifan Li, Aijun Sun, Jingyang Wang

“…Stock price data have the characteristics of time series. At the same time, based on machine learning long short-term memory (LSTM) which has the advantages of analyzing relationships among time series data through its memory function, we propose a forecasting method of stock price based on CNN-LSTM. …”
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Suicide Research and Adolescent Suicide Trends in New Zealand by Said Shahtahmasebi

“…Suicide time series appear to have a memory compounded with seasonal and cyclic effects. …”
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Statistical Prediction of the South China Sea Surface Height Anomaly by Caixia Shao, Weimin Zhang, Chunjian Sun, Xinmin Chai, Zhimin Wang

“…Analysis results demonstrate that the SODA SSHA time series are significantly correlated to the AVISO SSHA time series in SCS. …”
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Monitoring and Assessing the Changes in the Coverage and Decline of Oak Forests in Lorestan Province using Satellite Images and BFAST Model by hengameh shiravand, Shahriar Khaledi, Saeed Behzadi, Hojjat Allah sanjabi

“…A proposed method for identifying a general change in time series is use the BFAST model, which, by analyzing the time series in the process, season, and residual components, identifies the changes in the time series and also repeatedly estimates the time and amount of the changes, and The path and amount of variation in this study, using this model and satellite images to monitor and evaluate the changes in coverage and decline of oak forests in Lorestan province during the statistical period (2000-2017). …”
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Artificial Neural Network Modeling for Spatial and Temporal Variations of Pore-Water Pressure Responses to Rainfall by M. R. Mustafa, R. B. Rezaur, H. Rahardjo, M. H. Isa, A. Arif

“…To investigate applicability of the model for prediction of spatial and temporal variations of pore-water pressure, the model was tested for the time series data of pore-water pressure at multiple soil depths (i.e., 0.5 m, 1.1 m, 1.7 m, 2.3 m, and 2.9 m). …”
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An Improved Demand Forecasting Model Using Deep Learning Approach and Proposed Decision Integration Strategy for Supply Chain by Zeynep Hilal Kilimci, A. Okay Akyuz, Mitat Uysal, Selim Akyokus, M. Ozan Uysal, Berna Atak Bulbul, Mehmet Ali Ekmis

“…For this purpose, historical data can be analyzed to improve demand forecasting by using various methods like machine learning techniques, time series analysis, and deep learning models. In this work, an intelligent demand forecasting system is developed. …”
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Periods of constant wind speed: how long do they last in the turbulent atmospheric boundary layer? by D. Moreno, J. Friedrich, M. Wächter, J. Schwarte, J. Peinke

“…A comparison to wind time series generated with standard synthetic wind models and to time series from ideal stationary turbulence suggests that these structures are not characteristics of small-scale turbulence but seem to be consequences of larger-scale structures of the atmospheric boundary layer and thus are multi-scale. …”
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STAR‐ESDM: A Generalizable Approach to Generating High‐Resolution Climate Projections Through Signal Decomposition by Katharine Hayhoe, Ian Scott‐Fleming, Anne Stoner, Donald J. Wuebbles

“…Components are then recombined for each station or grid cell to produce a continuous, high‐resolution bias‐corrected and downscaled time series at the spatial and temporal scale of the predictand time series. …”
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Cycleanalysis oftime seriesof annualprecipitationHelehandMondwatershed by yadollah balyani, Mohammad saligheh, Hossein asakereh, Mohammad Hossein Nasserzadeh

“…On this basis, the Story of annual precipitation 95 percent for each of the stations under study and cycle meaningful estimate of the time series of basin data were extracted.…”
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Application of Residual-Based EWMA Control Charts for Detecting Faults in Variable-Air-Volume Air Handling Unit System by Haitao Wang

“…In order to provide a level of robustness with respect to modeling errors, control limits are determined by incorporating time series model uncertainty in EWMA control chart. …”
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Spectral study of COVID-19 pandemic in Japan: The dependence of spectral gradient on the population size of the community. by Ayako Sumi, Masayuki Koyama, Manato Katagiri, Norio Ohtomo

“…For prefectures with large population sizes, PSD patterns obtained from segment time series behave in response to the introduction of public and workplace vaccination programs as predicted by theoretical studies based on the SEIR model. …”
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Stability analysis of parameters of the GDP components models by Bronislava Kaminskienė, Vitalija Avdejenkova

“…The main problem is that time series are quite short and GDP is affected by results of work of single enterprises. …”
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Robust and Unbiased Variance of GLM Coefficients for Misspecified Autocorrelation and Hemodynamic Response Models in fMRI by Lourens Waldorp

“…The alternative, referred to as the sandwich, is based primarily on the fact that the time series are obtained from multiple exchangeable stimulus presentations. …”
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Bayesian Non-Parametric Mixtures of GARCH(1,1) Models by John W. Lau, Ed Cripps

“…However, nonstationary time series data may exhibit abrupt changes in volatility, suggesting changes in the underlying GARCH regimes. …”
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Synchronization Measure Based on a Geometric Approach to Attractor Embedding Using Finite Observation Windows by Inga Timofejeva, Kristina Poskuviene, Maosen Cao, Minvydas Ragulskis

“…A simple and effective algorithm for the identification of optimal time delays based on the geometrical properties of the embedded attractor is presented in this paper. A time series synchronization measure based on optimal time delays is derived. …”
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The Optimal Bandwidth Parameter Selection in GPH Estimation by Weijie Zhou, Huihui Tao, Feifei Wang, Weiqiang Pan

“…Firstly, combining with the stylized facts of financial time series, we generate long memory sequences by using the ARFIMA (1, d, 1) process. …”
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SAR Radiometric Cross-Calibration Based on Multiple Pseudoinvariant Calibration Sites With Extensive Backscattering Coefficient Range by Yongsheng Zhou, Xiang Chen, Qiang Yin, Fei Ma, Fan Zhang

“…Using time-series data from Sentinel-1, the study identifies 57 stable homogeneous targets as PICS, exhibiting a wide intensity distribution and a time-series RMSE less than 0.5 dB. …”
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EMD-GM-ARMA Model for Mining Safety Production Situation Prediction by Menglong Wu, Yicheng Ye, Nanyan Hu, Qihu Wang, Huimin Jiang, Wen Li

“…First of all, according to the nonstationary characteristics of the mining safety accident time series, nonstationary original time series were decomposed into high- and low-frequency signals using the EMD algorithm, which represents the overall trend and random disturbances, respectively. …”
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ECG Prediction Based on Classification via Neural Networks and Linguistic Fuzzy Logic Forecaster by Eva Volna, Martin Kotyrba, Hashim Habiballa

“…We also experimented with the new method of time series transparent prediction based on fuzzy transform with linguistic IF-THEN rules. …”
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Using Elman Neural Network Model to Forecast and Analyze the Agricultural Economy by Yucong You

“…To improve the accuracy of agricultural economic time series forecasting under the condition of complexity and diversity, this paper proposes an agricultural economic forecasting method based on Elman neural network structure. …”
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