Constructing Digitized Chaotic Time Series with a Guaranteed Enhanced Period by Chuanfu Wang, Qun Ding

“…In this paper, an approach based on the introduction of additional parameters to counteract the short periodic behavior of digitized chaotic time series is discussed. We analyze the ways that perturbation sources are introduced in parameters and variables and prove that the period of digitized chaotic time series generated by a digitized logistic map is improved efficiently. …”
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Hybrid Time Series Method for Long-Time Temperature Series Analysis by Guangdong Huang, Jiahong Li

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Signatures of Plastic Instabilities and Strain Localization in Acoustic Emission Time-Series by Alexey Vinogradov

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DWNet: Dual-Window Deep Neural Network for Time Series Prediction by Jin Fan, Yipan Huang, Ke Zhang, Sen Wang, Jinhua Chen, Baiping Chen

“…Multivariate time series prediction is a very important task, which plays a huge role in climate, economy, and other fields. …”
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Error Characteristic Analysis and Filtering Algorithm for GNSS Time-Series Data by Hongli Zhang, Yijin Chen, Kemeng Li, Yinggang Wang

Subjects: “…time series data…”
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Levenberg-Marquardt Algorithm for Mackey-Glass Chaotic Time Series Prediction by Junsheng Zhao, Yongmin Li, Xingjiang Yu, Xingfang Zhang

“…For decades, Mackey-Glass chaotic time series prediction has attracted more and more attention. …”
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On the Investigation of State Space Reconstruction of Nonlinear Aeroelastic Response Time Series by Flávio D. Marques, Eduardo M. Belo, Vilma A. Oliveira, José R. Rosolen, Andréia R. Simoni

“…Dynamic systems techniques based on time series analysis can be adequately applied to non-linear aeroelasticity. …”
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Forecasting insect abundance using time series embedding and machine learning by Gabriel R. Palma, Rodrigo F. Mello, Wesley A.C. Godoy, Eduardo Engel, Douglas Lau, Charles Markham, Rafael A. Moral

“…However, another layer of complexity is added when other covariates are considered in the forecasting, such as climate time series collected along the monitoring system. Multiple combinations of climate time series and their lags can be used to build a forecasting method. …”
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Estimating cash in bank branches by time series and neural network approaches by Pejman Peykani, Farzad Eshghi, Alireza Jandaghian, Hamed Farrokhi-Asl, Farid Tondnevis

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A Symbolic Algorithm for Checking the Identifiability of a Time-Series Model by Guy Mélard 

“…Several authors have attempted to compute the asymptotic Fisher information matrix for a univariate or multivariate time-series model to check for its identifiability. …”
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Modelling Customs Revenue in Ghana Using Novel Time Series Methods by Diana Ayorkor Agbenyega, John Andoh, Samuel Iddi, Louis Asiedu

“…Predominant amongst the existing models are the econometric models (the GDP-based model, the monthly receipts model, and the microsimulation model), which are laborious and sometimes unreliable when studying trends in time series data. In this study, we modelled monthly revenue data obtained from the Ghana Revenue Authority-Customs Division (GRA-CD) for the period January 2010 to December 2019 using two traditional time series models, ARIMA model and ARIMA Error Regression Model (ARIMAX), and two machine learning time series models, Bayesian Structural Time Series (BSTS) model and a Neural Network Autoregression model. …”
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Time Series Symbolization Method for the Data Mining K-Means Algorithm by Guisheng Wang

“…Time series is a data type frequently encountered in data analysis. …”
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LSTM+MA: A Time-Series Model for Predicting Pavement IRI by Tianjie Zhang, Alex Smith, Huachun Zhai, Yang Lu

“…In this work, a long short-term memory (LSTM)-based model, LSTM+MA, is proposed to predict the IRI of pavements using the time-series data extracted from the long-term pavement performance (LTPP) dataset. …”
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Fast Detection of Deceptive Reviews by Combining the Time Series and Machine Learning by Minjuan Zhong, Zhenjin Li, Shengzong Liu, Bo Yang, Rui Tan, Xilong Qu

“…Therefore, by focusing on the detection efficiency problem and the limitation of large amount of labeled examples dependence, in this paper, we proposed an effective semisupervised learning approach for detecting spam reviews. Firstly, a time series model of all the reviews of a product is constructed, and then the suspected time intervals are captured based on the burst review increases in these intervals. …”
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Comparative Analysis of Neural Networking and Regression Models for Time Series Forecasting by S. V. Sholtanyuk

“…Applicability of neural nets in time series forecasting has been considered and researched. …”
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Power Forecasting of Combined Heating and Cooling Systems Based on Chaotic Time Series by Liu Hai, Song Yong, Du Qingfu

“…The attractor of chaotic power time series can be reconstructed based on the embedding dimension and delay time in the phase space. …”
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Practical Minimum Sample Size for Road Crash Time-Series Prediction Models by Fady M. A. Hassouna, Khaled Al-Sahili

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Financial time series classification method based on low‐frequency approximate representation by Bing Liu, Huanhuan Cheng

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A Deep-Learning Prediction Model for Imbalanced Time Series Data Forecasting by Chenyu Hou, Jiawei Wu, Bin Cao, Jing Fan

Subjects: “…time series forecasting…”
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Analysis and Prediction of Hydraulic Support Load Based on Time Series Data Modeling by Yi-Hui Pang, Hong-Bo Wang, Jian-Jian Zhao, De-Yong Shang

“…In this paper, the hydraulic support load data is decomposed into trend term, cycle term, and residual term, and it is found that the data has clear trend and period features, which can be called time series data. Based on the autoregression theory and weighted moving average method, the time series model is built to analyze the load data and predict its evolution trend, and the prediction accuracy of the sliding window model, ARIMA (Autoregressive Integrated Moving Average) model, and SARIMA (Seasonal Autoregressive Integrated Moving Average) model to the hydraulic support load under different parameters are evaluated, respectively. …”
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