Deep Fusion Prediction Method for Nonstationary Time Series Based on Feature Augmentation and Extraction

Deep learning effectively identifies and predicts modes but faces performance reduction under few-shot learning conditions. In this paper, a time series prediction framework for small samples is proposed, including a data augmentation algorithm, time series trend decomposition, multi-model prediction, and error-based fusion. First, data samples are augmented by retaining and extracting time series features. Second, the expanded data are decomposed based on data trends, and then, multiple deep models are used for prediction. Third, the models’ predictive outputs are combined with an error estimate from the intersection of covariances. Finally, the method is verified using natural systems and classic small-scale simulation datasets. The results show that the proposed method can improve the prediction accuracy of small sample sets with data augmentation and multi-model fusion.

[1]  Yanghua Xiao,et al.  A Fine-Grained Recognition Neural Network with High-Order Feature Maps via Graph-Based Embedding for Natural Bird Diversity Conservation , 2023, International journal of environmental research and public health.

[2]  Tingli Su,et al.  BMAE-Net: A Data-Driven Weather Prediction Network for Smart Agriculture , 2023, Agronomy.

[3]  P. Chakrabarti,et al.  Variational Bayesian Network with Information Interpretability Filtering for Air Quality Forecasting , 2023, Mathematics.

[4]  P. Chakrabarti,et al.  Deep Spatio-Temporal Graph Network with Self-Optimization for Air Quality Prediction , 2023, Entropy.

[5]  Chia-Nan Wang,et al.  A Two-Stage DEA Approach to Measure Operational Efficiency in Vietnam’s Port Industry , 2022, Mathematics.

[6]  Aytuğ Onan.  Bidirectional convolutional recurrent neural network architecture with group-wise enhancement mechanism for text sentiment classification , 2022, J. King Saud Univ. Comput. Inf. Sci..

[7]  Huiming Tang,et al.  Equation Chapter 1 Section 0A comparative study of different machine learning methods for reservoir landslide displacement prediction , 2022, Engineering Geology.

[8]  Renquan Lu,et al.  Adaptive Attitude Control of a Quadrotor Using Fast Nonsingular Terminal Sliding Mode , 2021, IEEE Transactions on Industrial Electronics.

[9]  Jinhui Tang,et al.  Spatiotemporal Co-Attention Recurrent Neural Networks for Human-Skeleton Motion Prediction , 2019, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[10]  Feng Lin,et al.  A Hybrid Deep Learning Model With Attention-Based Conv-LSTM Networks for Short-Term Traffic Flow Prediction , 2021, IEEE Transactions on Intelligent Transportation Systems.

[11]  B. Esakki,et al.  Desired tracking of delayed quadrotor UAV under model uncertainty and wind disturbance using adaptive super-twisting terminal sliding mode control. , 2021, ISA transactions.

[12]  Okke Batelaan,et al.  Ensemble machine learning paradigms in hydrology: A review , 2021, Journal of Hydrology.

[13]  T. Dobbins,et al.  Interrupted time series analysis using autoregressive integrated moving average (ARIMA) models: a guide for evaluating large-scale health interventions , 2021, BMC Medical Research Methodology.

[14]  Maiying Zhong,et al.  Fault Diagnosis of Rolling Bearings Based on an Improved Stack Autoencoder and Support Vector Machine , 2021, IEEE Sensors Journal.

[15]  Hongjun Lin,et al.  New methods based on back propagation (BP) and radial basis function (RBF) artificial neural networks (ANNs) for predicting the occurrence of haloketones in tap water. , 2021, The Science of the total environment.

[16]  Bryan Lim,et al.  Time-series forecasting with deep learning: a survey , 2020, Philosophical Transactions of the Royal Society A.

[17]  Alice Buffi,et al.  Sensor-Fusion and Tracking Method for Indoor Vehicles With Low-Density UHF-RFID Tags , 2021, IEEE Transactions on Instrumentation and Measurement.

[18]  Yuetian Liu,et al.  A data-driven shale gas production forecasting method based on the multi-objective random forest regression , 2021 .

[19]  Yang-Ping Yao,et al.  Machine learning for pore-water pressure time-series prediction: Application of recurrent neural networks , 2021 .

[20]  B. Seong.  Smoothing and forecasting mixed-frequency time series with vector exponential smoothing models , 2020 .

[21]  Taesam Lee.  EMD and LSTM Hybrid Deep Learning Model for Predicting Sunspot Number Time Series with a Cyclic Pattern , 2020, Solar Physics.

[22]  Yuting Bai,et al.  Group Decision-Making Support for Sustainable Governance of Algal Bloom in Urban Lakes , 2020, Sustainability.

[23]  Zhiyao Zhao,et al.  An Action Dependent Heuristic Dynamic Programming Approach for Algal Bloom Prediction With Time-Varying Parameters , 2020, IEEE Access.

[24]  Walery Rogoza,et al.  Method for the prediction of time series using small sets of experimental samples , 2019, Appl. Math. Comput..

[25]  Li Tang,et al.  Predicting the direction of stock markets using optimized neural networks with Google Trends , 2018, Neurocomputing.

[26]  Chongguang Li,et al.  Seasonal forecasting of agricultural commodity price using a hybrid STL and ELM method: Evidence from the vegetable market in China , 2018, Neurocomputing.

[27]  Tao Li,et al.  Deep belief echo-state network and its application to time series prediction , 2017, Knowl. Based Syst..

[28]  R. Rajesh,et al.  Forecasting supply chain resilience performance using grey prediction , 2016, Electron. Commer. Res. Appl..

[29]  Dongxiao Niu,et al.  Short-term wind speed forecasting using wavelet transform and support vector machines optimized by genetic algorithm , 2014 .

[30]  Fan Min,et al.  Forecasting method for water quality time series of few and abnormal data , 2010 .

[31]  A. Luati,et al.  Global and local statistical properties of fixed-length nonparametric smoothers , 2002 .

[32]  S. Mandal.  Discussion of “Back‐Propagation Neural Network in Tidal‐Level Forecasting” by S. Mandal , 2001 .

[33]  Ching-Piao Tsai,et al.  BACK-PROPAGATION NEURAL NETWORK IN TIDAL-LEVEL FORECASTING , 2001 .

[34]  R. Engle.  Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation , 1982 .

[35]  John F. MacGregor,et al.  Some Recent Advances in Forecasting and Control , 1968 .