Low Complexity, Low Probability Patterns and Consequences for Algorithmic Probability Applications by Mohammad Alaskandarani, Kamaludin Dingle

“…Here, we study this low complexity, low probability phenomenon by looking at example maps, namely a finite state transducer, natural time series data, RNA molecule structures, and polynomial curves. …”
Get full text

Multiscale Permutation Entropy Based Rolling Bearing Fault Diagnosis by Jinde Zheng, Junsheng Cheng, Yu Yang

“…Permutation entropy (PE) was recently proposed and defined to measure the randomicity and detect dynamical changes of time series. However, for the complexity of mechanical systems, the randomicity and dynamic changes of the vibration signal will exist in different scales. …”
Get full text

The Multiplex Dependency Structure of Financial Markets by Nicolò Musmeci, Vincenzo Nicosia, Tomaso Aste, Tiziana Di Matteo, Vito Latora

“…In particular, we consider multiplex networks made of four layers corresponding, respectively, to linear, nonlinear, tail, and partial correlations among a set of financial time series. We construct the sparse graph on each layer using a standard network filtering procedure, and we then analyse the structural properties of the obtained multiplex networks. …”
Get full text

Research on Supply Chain Stability Driven by Consumer’s Channel Preference Based on Complexity Theory by Yi Tian, Junhai Ma, Wandong Lou

“…In addition, the dynamic features of the system are simulated by 2D bifurcation diagram, the largest Lyapunov exponent, attractor variation, and time series. The simulation results suggest that if the adjustment speeds of the wholesale prices and sales commissions change drastically, the system would fall into a chaotic state. …”
Get full text

Dynamic Reliability Prediction of Bridges Based on Decoupled SHM Extreme Stress Data and Improved BDLM by Xueping Fan, Yuefei Liu

“…In this paper, considering the coupling, randomness, and time variation of SHM data, firstly, the coupled extreme stress data, which are considered as a time series, are decoupled into high-frequency and low-frequency data with the moving average method. …”
Get full text

An optimized method for short-term load forecasting based on feature fusion and ConvLSTM-3D neural network by Xiaofeng Yang, Shousheng Zhao, Kangyi Li, Wenjin Chen, Si Zhang, Jingwei Chen

“…A time attention mechanism is then applied to fuse these features based on their correlation weights, enhancing their impact within the time series. Finally, the ConvLSTM-3D model is trained on the fused features to generate short-term load forecasts. …”
Get full text

Financial Futures Prediction Using Fuzzy Rough Set and Synthetic Minority Oversampling Technique by Shangkun Deng, Yingke Zhu, Ruijie Liu, Wanyu Xu

“…Then, the FRS- (fuzzy rough set-) based method, as an efficient tool for analyzing complex and nonlinear information with high noise and uncertainty of financial time series, is adopted for the price change multiclassification of the CSI300 futures. …”
Get full text

GRAiCE: reconstructing terrestrial water storage anomalies with recurrent neural networks by Irene Palazzoli, Serena Ceola, Pierre Gentine

“…By generating long-term continuous TWSA time series, GRAiCE will offer valuable insights into the impacts of climate variability and change on freshwater resources.…”
Get full text

A novel prediction model of grounding resistance based on long short-term memory by Xinghai Pu, Jing Zhang, Fei Wang, Shuai Xue

“…This study aims to investigate the use of Long Short-Term Memory (LSTM) models for predicting temporal variations in grounding resistance using time series data. This analysis is the first to apply LSTM models to grounding resistance prediction, utilizing experimental data, including soil resistivity and rainfall. …”
Get full text

Seasonal to Decadal Western Boundary Current Variability From Sustained Ocean Observations by M. Chandler, N. V. Zilberman, J. Sprintall

“…The resulting 16 year time series (2004–2019) show a weakening trend in Kuroshio transport, but no trend in Agulhas transport or East Australian Current transport. …”
Get full text

Nonlinear connectedness of conventional crypto-assets and sustainable crypto-assets with climate change: A complex systems modelling approach. by Mushtaq Hussain Khan, Shreya Macherla, Angesh Anupam

“…Earlier studies used classical time series models to forecast the nonlinear connectedness of conventional crypto-assets with CO2 emissions. …”
Get full text

Climate Change Effects on Crop Area Dynamics in the Cachar District of Assam, India: An Empirical Study by Mashud Ahmed, Md Kamrul Islam and Samar Das

“…This study aims to investigate climate change effects on crop area dynamics in the Cachar district of Assam, India, for a period spanning from 1981 to 2017. The time series ARDL (Autoregressive Distributed Lag) model is employed to analyze the relationship between climate factors and areas under different crops. …”
Get full text

Vessel Navigation Behavior Analysis and Multiple-Trajectory Prediction Model Based on AIS Data by He Ma, Yi Zuo, Tieshan Li

“…Second, we design an integrated model for simultaneous prediction of multiple trajectories using the proposed features and employ the long short-term memory (LSTM)-based neural network and recurrent neural network (RNN) to pursue this time series task. Furthermore, the comparative experiments prove that the mean value and standard deviation of root mean squared error (RMSE) using the LSTM are 4% and 14% lower than those using the RNN, respectively.…”
Get full text

Hybrid Deep Learning Techniques for Improved Anomaly Detection in IoT Environments by Hanan Abbas Mohammad

“…There are numerous deep learning competencies, but LSTM is one of the ones used to interpret big data or time series data. But, it is not easy to find what is the best weights for LSTMs in order to directly achieve performance. …”
Get full text

MPD-Model: A Distributed Multipreference-Driven Data Fusion Model and Its Application in a WSNs-Based Healthcare Monitoring System by Jibing Gong, Li Cui, Kejiang Xiao, Rui Wang

“…Next, to implement feature extraction of wrist-pulse data, we propose FEA, a light-weight adaptive feature extraction algorithm for time series sensed data. Simultaneously, we design TFD-Pattern that is a unique human pulse pattern. …”
Get full text

Research on Multistage Dynamic Trading Model Based on Gray Model and Auto-Regressive Integrated Moving Average Model by Zishan Xu, Chuanggeng Lin, Zhe Zhuang, Lidong Wang

“…This model analyzes and forecasts daily price data by establishing a combination forecasting model of the gray GM (1,1) model and the ARIMA time series model and establishes a multiobjective dynamic programming model with moving average convergence divergence (MACD) and Sharpe ratio indicators as risk constraints to formulate appropriate investment trading strategies. …”
Get full text

A Novel Highly Nonlinear Quadratic System: Impulsive Stabilization, Complexity Analysis, and Circuit Designing by Arthanari Ramesh, Alireza Bahramian, Hayder Natiq, Karthikeyan Rajagopal, Sajad Jafari, Iqtadar Hussain

“…The PSpice simulations confirm the theoretical analysis. The oscillator’s time series complexity is also investigated using sample entropy. …”
Get full text

On a Memristor-Based Hyperchaotic Circuit in the Context of Nonlocal and Nonsingular Kernel Fractional Operator by Shahram Rezapour, Chernet Tuge Deressa, Sina Etemad

“…It is shown using phase-space portraits and time-series orbit figures that the system is sensitive to derivative order change, parameter change, and small initial condition change. …”
Get full text

Clustering analysis of Yue opera character tone trends based on quantum particle swarm optimization for fuzzy C-means. by Yuhang Zhang, Xiaofeng Wu, Jiawei Xu, Zihao Ning, Xiao Han

“…Linear interpolation is applied to process the time series data of vocal melodies, addressing inconsistent feature dimensions. …”
Get full text

Track Circuits Fault Diagnosis Method Based on the UNet-LSTM Network (ULN) by Weijie Tao, Xiaowei Li, Zheng Li

“…Considering that the fault data are a one-dimensional time series, this paper presents a fault diagnosis method based on the UNet-LSTM network (ULN). …”
Get full text