scholarly journals Hydrological time series prediction by extreme machine learning and sparrow search algorithm

2021 ◽  
Author(s):  
Bao-fei Feng ◽  
Yin-shan Xu ◽  
Tao Zhang ◽  
Xiao Zhang
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Search Algorithm ◽  
Time Series Prediction ◽  
Single Layer ◽  
Classification Problems ◽  
Water Resource System ◽  
Evaluation Indexes ◽  
Runoff Series ◽  
Prediction Problems

Abstract In general, accurate hydrological time series prediction information is of great significance for the rational planning and management of water resource system. Extreme learning machine (ELM) is an effective tool proposed for the single-layer feedforward neural network in the regression and classification problems. However, the standard ELM model falls into local minimum with a high probability in hydrological prediction problems since the randomly assigned parameters (like input-hidden weights and hidden biases) often remain unchanged at the learning process. For effectively improving the prediction accuracy, this paper develops a hybrid hydrological forecasting model where the emerging sparrow search algorithm (SSA) is firstly used to determine the satisfying parameter combinations of the ELM model, and then the Moore-Penrose generalized inverse method is chosen to analytically obtain the weight matrix between the hidden layer and output layer. The proposed method is used to forecast the long-term daily runoff series collected from three real-world hydrological stations in China. Based on several performance evaluation indexes, the results show that the proposed method outperforms several ELM variants optimized by other evolutionary algorithms in both training and testing phases. Hence, an effective evolutionary machine learning tool is developed for accurate hydrological time series forecasting. HIGHLIGHT Hydrologic forecasting, sparrow search algorithm, extreme machine learning.

scholarly journals FASTER TRAINING USING FUSION OF ACTIVATION FUNCTIONS FOR FEED FORWARD NEURAL NETWORKS

2009 ◽  
Vol 19 (06) ◽  
pp. 437-448 ◽  
Author(s):  
MD. ASADUZZAMAN ◽  
MD. SHAHJAHAN ◽  
KAZUYUKI MURASE
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Error Function ◽  
Time Series Prediction ◽  
Back Propagation ◽  
Activation Functions ◽  
Classification Problems ◽  
Feed Forward ◽  
Feed Forward Neural Networks ◽  
Prediction Problems

Multilayer feed-forward neural networks are widely used based on minimization of an error function. Back propagation (BP) is a famous training method used in the multilayer networks but it often suffers from the drawback of slow convergence. To make the learning faster, we propose 'Fusion of Activation Functions' (FAF) in which different conventional activation functions (AFs) are combined to compute final activation. This has not been studied extensively yet. One of the sub goals of the paper is to check the role of linear AFs in combination. We investigate whether FAF can enable the learning to be faster. Validity of the proposed method is examined by performing simulations on challenging nine real benchmark classification and time series prediction problems. The FAF has been applied to 2-bit, 3-bit and 4-bit parity, the breast cancer, Diabetes, Heart disease, Iris, wine, Glass and Soybean classification problems. The algorithm is also tested with Mackey-Glass chaotic time series prediction problem. The algorithm is shown to work better than other AFs used independently in BP such as sigmoid (SIG), arctangent (ATAN), logarithmic (LOG).

scholarly journals Predictive Error Compensating Wavelet Neural Network Model for Multivariable Time Series Prediction

TEM Journal ◽  
2021 ◽  
pp. 1955-1963
Author(s):  
Ajla Kulaglic ◽  
B. Berk Ustundag
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Time Series ◽  
Short Term Memory ◽  
Time Series Prediction ◽  
Wavelet Neural Network ◽  
Training Dataset ◽  
Lorenz Attractor ◽  
Predictive Error ◽  
Prediction Problems

Multivariable machine learning (ML) models are increasingly used for time series predictions. However, avoiding the overfitting and underfitting in ML-based time series prediction requires special consideration depending on the size and characteristics of the available training dataset. Predictive error compensating wavelet neural network (PEC-WNN) improves the time series prediction accuracy by enhancing the orthogonal features within a data fusion scheme. In this study, time series prediction performance of the PEC-WNNs have been evaluated on two different problems in comparison to conventional machine learning methods including the long short-term memory (LSTM) network. The results have shown that PECNET provides significantly more accurate predictions. RMSPE error is reduced by more than 60% with respect to other compared ML methods for Lorenz Attractor and wind speed prediction problems.

scholarly journals Application of Deep Learning Architectures for Satellite Image Time Series Prediction: A Review

2021 ◽  
Vol 13 (23) ◽  
pp. 4822
Author(s):  
Waytehad Rose Moskolaï ◽  
Wahabou Abdou ◽  
Albert Dipanda ◽  
Kolyang
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Deep Learning ◽  
Satellite Images ◽  
Spatial Information ◽  
Time Series Prediction ◽  
Satellite Image ◽  
Machine Learning Algorithms ◽  
Temporal Dimension ◽  
Prediction Problems

Satellite image time series (SITS) is a sequence of satellite images that record a given area at several consecutive times. The aim of such sequences is to use not only spatial information but also the temporal dimension of the data, which is used for multiple real-world applications, such as classification, segmentation, anomaly detection, and prediction. Several traditional machine learning algorithms have been developed and successfully applied to time series for predictions. However, these methods have limitations in some situations, thus deep learning (DL) techniques have been introduced to achieve the best performance. Reviews of machine learning and DL methods for time series prediction problems have been conducted in previous studies. However, to the best of our knowledge, none of these surveys have addressed the specific case of works using DL techniques and satellite images as datasets for predictions. Therefore, this paper concentrates on the DL applications for SITS prediction, giving an overview of the main elements used to design and evaluate the predictive models, namely the architectures, data, optimization functions, and evaluation metrics. The reviewed DL-based models are divided into three categories, namely recurrent neural network-based models, hybrid models, and feed-forward-based models (convolutional neural networks and multi-layer perceptron). The main characteristics of satellite images and the major existing applications in the field of SITS prediction are also presented in this article. These applications include weather forecasting, precipitation nowcasting, spatio-temporal analysis, and missing data reconstruction. Finally, current limitations and proposed workable solutions related to the use of DL for SITS prediction are also highlighted.

scholarly journals Time-Series Prediction of Iron and Silicon Content in Aluminium Electrolysis Based on Machine Learning

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 10699-10710
Author(s):  
Linsheng Chen ◽  
Yongming Wu ◽  
Yingbo Liu ◽  
Tiansong Liu ◽  
Xiaojing Sheng
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Silicon Content ◽  
Time Series Prediction ◽  
Aluminium Electrolysis

Discovering Predictable Classifications

1993 ◽  
Vol 5 (4) ◽  
pp. 625-635 ◽  
Author(s):  
Jürgen Schmidhuber ◽  
Daniel Prelinger
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Time Series Prediction ◽  
Learning Problems ◽  
Common Learning ◽  
Prediction Control ◽  
Prediction Problems

Prediction problems are among the most common learning problems for neural networks (e.g., in the context of time series prediction, control, etc.). With many such problems, however, perfect prediction is inherently impossible. For such cases we present novel unsupervised systems that learn to classify patterns such that the classifications are predictable while still being as specific as possible. The approach can be related to the IMAX method of Becker and Hinton (1989) and Zemel and Hinton (1991). Experiments include a binary stereo task proposed by Becker and Hinton, which can be solved more readily by our system.

scholarly journals Time Series Prediction Based on Complex-Valued S-System Model

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Bin Yang ◽  
Wenzheng Bao ◽  
Yuehui Chen
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Financial Time Series ◽  
Time Series Prediction ◽  
System Model ◽  
Series Data ◽  
Classification Problems ◽  
Mathematical Formulas ◽  
Input Variables ◽  
Complex Valued

Symbolic regression has been utilized to infer mathematical formulas in order to solve the complex prediction and classification problems. In this paper, complex-valued S-system model (CVSS) is proposed to predict real-valued time series data. In a CVSS model, input variables and rate constants are complex-valued. The time series data need to be translated into complex numbers. The hybrid evolutionary algorithm based on complex-valued restricted additive tree and firefly algorithm is proposed to search the optimal CVSS model. Three financial time series data and Mackey–Glass chaos time series are collected to evaluate our proposed method. The experiment results show that the predicted data are very close to the target ones and our method could obtain the better RMSE, MAP, MAPE, POCID, R2, and ARV performances than ARIMA, radial basis function neural network (RBFNN), flexible neural tree (FNT), ordinary differential equation (ODE), and S-system.

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