Chaotic time series forecasting approaches using machine learning techniques: A review
B Ramadevi, K Bingi - Symmetry, 2022 - mdpi.com
Traditional statistical, physical, and correlation models for chaotic time series prediction
have problems, such as low forecasting accuracy, computational time, and difficulty …
have problems, such as low forecasting accuracy, computational time, and difficulty …
[HTML][HTML] Rainfall prediction by using ANFIS times series technique in South Tangerang, Indonesia
Excessive rainfall is one of the triggers for the flooding phenomenon, especially in the
tropics with flat or concave areas. Some critical points in the South Tangerang region, which …
tropics with flat or concave areas. Some critical points in the South Tangerang region, which …
Refined nonuniform embedding for coupling detection in multivariate time series
State-space reconstruction is essential to analyze the dynamics and internal interactions of
complex systems. However, it is difficult to estimate high-dimensional conditional mutual …
complex systems. However, it is difficult to estimate high-dimensional conditional mutual …
Nonuniform state space reconstruction for multivariate chaotic time series
State space reconstruction is the foundation of chaotic system modeling. Selection of
reconstructed variables is essential to the analysis and prediction of multivariate chaotic time …
reconstructed variables is essential to the analysis and prediction of multivariate chaotic time …
Federated learning framework for mobile edge computing networks
R Fantacci, B Picano - CAAI Transactions on Intelligence …, 2020 - Wiley Online Library
The continuous growth of smart devices needing processing has led to moving storage and
computation from cloud to the network edges, giving rise to the edge computing paradigm …
computation from cloud to the network edges, giving rise to the edge computing paradigm …
A support vector based hybrid forecasting model for chaotic time series: Spare part consumption prediction
S Sareminia - Neural Processing Letters, 2023 - Springer
Reliability of spare parts inventory in the company is one of the most significant challenges
in the field of maintenance and repairs, but on the other hand, the liquidity crisis resulting …
in the field of maintenance and repairs, but on the other hand, the liquidity crisis resulting …
t-SNE and variational auto-encoder with a bi-LSTM neural network-based model for prediction of gas concentration in a sealed-off area of underground coal mines
A deep learning network is introduced to predict concentrations of gases in the underground
coal mine enclosed region using various IoT-enabled gas sensors installed in a metallic gas …
coal mine enclosed region using various IoT-enabled gas sensors installed in a metallic gas …
DeepPIPE: A distribution-free uncertainty quantification approach for time series forecasting
Time series forecasting is a challenging task as the underlying data generating process is
dynamic, nonlinear, and uncertain. Deep learning such as LSTM and auto-encoder can …
dynamic, nonlinear, and uncertain. Deep learning such as LSTM and auto-encoder can …
Traffic flow prediction using neural network
Traffic flow management and analysis have become essential for both individuals to better
manage and route their daily commutes, and for transportation planners to optimally …
manage and route their daily commutes, and for transportation planners to optimally …
Analysis of electric energy consumption profiles using a machine learning approach: A Paraguayan case study
F Morales, M García-Torres, G Velázquez… - Electronics, 2022 - mdpi.com
Correctly defining and grouping electrical feeders is of great importance for electrical system
operators. In this paper, we compare two different clustering techniques, K-means and …
operators. In this paper, we compare two different clustering techniques, K-means and …