scholarly journals FPGA-Based Implementation of a Multilayer Perceptron Suitable for Chaotic Time Series Prediction

Technologies ◽  
2018 ◽  
Vol 6 (4) ◽  
pp. 90 ◽  
Author(s):  
Ana Pano-Azucena ◽  
Esteban Tlelo-Cuautle ◽  
Sheldon Tan ◽  
Brisbane Ovilla-Martinez ◽  
Luis de la Fraga
Keyword(s):  
Time Series ◽  
Multilayer Perceptron ◽  
Time Series Data ◽  
Mean Squared Error ◽  
Time Series Prediction ◽  
Chaotic Time Series ◽  
Series Data ◽  
Support Vector ◽  
Maximum Lyapunov Exponent ◽  
Inference System

Many biological systems and natural phenomena exhibit chaotic behaviors that are saved in time series data. This article uses time series that are generated by chaotic oscillators with different values of the maximum Lyapunov exponent (MLE) to predict their future behavior. Three prediction techniques are compared, namely: artificial neural networks (ANNs), the adaptive neuro-fuzzy inference system (ANFIS) and least-squares support vector machines (SVM). The experimental results show that ANNs provide the lowest root mean squared error. That way, we introduce a multilayer perceptron that is implemented using a field-programmable gate array (FPGA) to predict experimental chaotic time series.

scholarly journals Wavelet discrete transform, ANFIS and linear regression for short-term time series prediction of air temperature

2017 ◽  
Vol 3 (2) ◽  
pp. 68 ◽  
Author(s):  
Devi Munandar
Keyword(s):  
Time Series ◽  
Linear Regression ◽  
Time Series Data ◽  
Mean Squared Error ◽  
Pearson Correlation ◽  
Time Series Prediction ◽  
Coefficient Of Determination ◽  
Series Data ◽  
Discrete Wavelet ◽  
Inference System

This paper investigates the ability of Discrete Wavelet Transform and Adaptive Network-Based Fuzzy Inference System in time-series data modeling of weather parameters. Plotting predicted data results on Linear Regression is used as the baseline of the statistical model. Data were tested in every 10 minutes interval on weather station of Bungus port in Padang, Indonesia. Mean absolute errors (MAE), the coefficient of determination (R2), Pearson correlation coefficient (r) and root mean squared error (RMSE) are used as performance indicators. The result of Plotting ANFIS data against linear regression using 1-input data is the optimal values combination of output predictions.

Forecasting Rice Production in West Bengal State in India

2013 ◽  
Vol 4 (4) ◽  
pp. 68-91 ◽  
Author(s):  
Arindam Chaudhuri
Keyword(s):  
Time Series ◽  
Multiple Regression ◽  
Computational Intelligence ◽  
Time Series Data ◽  
Rice Production ◽  
Productivity Index ◽  
Series Data ◽  
Support Vector ◽  
Production Time ◽  
Inference System

Forecasting rice production is a challenging problem in agricultural statistics. The inherent difficulty lies in demand and supply affected by many uncertain factors viz. economic policies, agricultural factors, credit measures, foreign trade etc. which interact in a complex manner. Since last few decades, Statistical techniques are used for developing predictive models to estimate required parameters. Determination of nature of rice production time series data is difficult, expensive, time consuming and involves tedious tests. In this paper, we use Interval Type Fuzzy Auto Regressive Integrated Moving Average (ITnARIMA), Adaptive Neuro Fuzzy Inference System (ANFIS) and Modified Regularized Least Squares Fuzzy Support Vector Regression (MRLSFSVR) for prediction of Productivity Index percent (PI %) of rice production time series data and compare it with traditional Statistical tool of Multiple Regression. The accuracies of ITnARIMA and ANFIS techniques are evaluated as relatively similar. It is found that ANFIS exhibits high performance than ITnARIMA, MRLSFSVR and Multiple Regression for predicting PI %. The performance comparison shows that Computational Intelligence paradigm is a promising tool for minimizing uncertainties in rice production data. Further Computational Intelligence techniques also minimize potential inconsistency of correlations.

scholarly journals Analisis Pola Prediksi Data Time Series menggunakan Support Vector Regression, Multilayer Perceptron, dan Regresi Linear Sederhana

2019 ◽  
Vol 3 (2) ◽  
pp. 282-287
Author(s):  
Ika Oktavianti ◽  
Ermatita Ermatita ◽  
Dian Palupi Rini
Keyword(s):  
Time Series ◽  
Support Vector Regression ◽  
Multilayer Perceptron ◽  
Time Series Data ◽  
Training Data ◽  
Machine Learning Techniques ◽  
Series Data ◽  
Support Vector ◽  
Contributing Factors ◽  
Testing Data

Licensing services is one of the forms of public services that important in supporting increased investment in Indonesia and is currently carried out by the Investment and Licensing Services Department. The problems that occur in general are the length of time to process licenses and one of the contributing factors is the limited number of licensing officers. Licensing data is a time series data which have monthly observation. The Artificial Neural Network (ANN) and Support Vector Machine (SVR) is used as machine learning techniques to predict licensing pattern based on time series data. Of the data used dataset 1 and dataset 2, the sharing of training data and testing data is equal to 70% and 30% with consideration that training data must be more than testing data. The result of the study showed for Dataset 1, the ANN-Multilayer Perceptron have a better performance than Support Vector Regression (SVR) with MSE, MAE and RMSE values is 251.09, 11.45, and 15.84. Then for dataset 2, SVR-Linear has better performance than MLP with values of MSE, MAE and RMSE of 1839.93, 32.80, and 42.89. The dataset used to predict the number of permissions is dataset 2. The study also used the Simple Linear Regression (SLR) method to see the causal relationship between the number of licenses issued and licensing service officers. The result is that the relationship between the number of licenses issued and the number of service officers is less significant because there are other factors that affect the number of licenses.  

scholarly journals A Novel Time Series Prediction Approach Based on a Hybridization of Least Squares Support Vector Regression and Swarm Intelligence

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Nhat-Duc Hoang ◽  
Anh-Duc Pham ◽  
Minh-Tu Cao
Keyword(s):  
Time Series ◽  
Least Squares ◽  
Support Vector Regression ◽  
Time Series Data ◽  
Time Series Prediction ◽  
Hybrid Approach ◽  
Series Data ◽  
Support Vector ◽  
Promising Alternative ◽  
Prediction Approach

This research aims at establishing a novel hybrid artificial intelligence (AI) approach, named as firefly-tuned least squares support vector regression for time series prediction(FLSVRTSP). The proposed model utilizes the least squares support vector regression (LS-SVR) as a supervised learning technique to generalize the mapping function between input and output of time series data. In order to optimize the LS-SVR’s tuning parameters, theFLSVRTSPincorporates the firefly algorithm (FA) as the search engine. Consequently, the newly construction model can learn from historical data and carry out prediction autonomously without any prior knowledge in parameter setting. Experimental results and comparison have demonstrated that theFLSVRTSPhas achieved a significant improvement in forecasting accuracy when predicting both artificial and real-world time series data. Hence, the proposed hybrid approach is a promising alternative for assisting decision-makers to better cope with time series prediction.

Performance degradation prediction of aeroengine based on attention model and support vector regression

2021 ◽  
pp. 095441002110147
Author(s):  
Changchang Che ◽  
Huawei Wang ◽  
Xiaomei Ni ◽  
Qiang Fu
Keyword(s):  
Time Series ◽  
Support Vector Regression ◽  
Time Series Data ◽  
Time Series Prediction ◽  
Performance Degradation ◽  
Series Data ◽  
Support Vector ◽  
Attention Model ◽  
Multiple State ◽  
And Performance

Accurate performance degradation prediction of aeroengines can ensure the safety and reliability of the aircraft. Based on the mass long time series data of multiple state parameters, a novel performance degradation prediction method based on attention model (AM) and support vector regression (SVR) is proposed in this article. The AM uses the attention mechanism between encoder and decoder to realize weight distribution of different source samples, so as to realize time series prediction of state parameters. The SVR model is used to mine the mapping relationship between multiple state parameters and performance degradation. The performance degradation prediction results can be achieved by putting the time series prediction results of multiple state parameters into the SVR model. The turbofan engine degradation simulation dataset carried out using commercial modular aero-propulsion system simulation (C-MAPSS) is used to verify the effectiveness of the proposed method. The results demonstrate that it can get accurate time series prediction and performance degradation analysis results. Compared with other methods, the proposed attention model and support vector regression (AM-SVR) model has lower prediction error and higher stability when dealing with noised samples.

Neural Network Techniques for Time Series Prediction: A Review

2019 ◽  
Vol 3 (3) ◽  
Author(s):  
Muhammad Faheem Mushtaq ◽  
Urooj Akram ◽  
Muhammad Aamir ◽  
Haseeb Ali ◽  
Muhammad Zulqarnain
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Time Series ◽  
Time Series Data ◽  
Weather Prediction ◽  
Time Series Prediction ◽  
Series Data ◽  
Prediction Problem ◽  
Neural Network Models ◽  
Physical Time

It is important to predict a time series because many problems that are related to prediction such as health prediction problem, climate change prediction problem and weather prediction problem include a time component. To solve the time series prediction problem various techniques have been developed over many years to enhance the accuracy of forecasting. This paper presents a review of the prediction of physical time series applications using the neural network models. Neural Networks (NN) have appeared as an effective tool for forecasting of time series.  Moreover, to resolve the problems related to time series data, there is a need of network with single layer trainable weights that is Higher Order Neural Network (HONN) which can perform nonlinearity mapping of input-output. So, the developers are focusing on HONN that has been recently considered to develop the input representation spaces broadly. The HONN model has the ability of functional mapping which determined through some time series problems and it shows the more benefits as compared to conventional Artificial Neural Networks (ANN). The goal of this research is to present the reader awareness about HONN for physical time series prediction, to highlight some benefits and challenges using HONN.

scholarly journals Superiority of Hybrid Soft Computing Models in Daily Suspended Sediment Estimation in Highly Dynamic Rivers

2021 ◽  
Vol 13 (2) ◽  
pp. 542
Author(s):  
Tarate Suryakant Bajirao ◽  
Pravendra Kumar ◽  
Manish Kumar ◽  
Ahmed Elbeltagi ◽  
Alban Kuriqi
Keyword(s):  
Time Series ◽  
Wavelet Transform ◽  
Suspended Sediment ◽  
River Basin ◽  
Time Series Data ◽  
Series Data ◽  
Discrete Wavelet ◽  
Original Time Series ◽  
Inference System ◽  
Original Time

Estimating sediment flow rate from a drainage area plays an essential role in better watershed planning and management. In this study, the validity of simple and wavelet-coupled Artificial Intelligence (AI) models was analyzed for daily Suspended Sediment (SSC) estimation of highly dynamic Koyna River basin of India. Simple AI models such as the Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) were developed by supplying the original time series data as an input without pre-processing through a Wavelet (W) transform. The hybrid wavelet-coupled W-ANN and W-ANFIS models were developed by supplying the decomposed time series sub-signals using Discrete Wavelet Transform (DWT). In total, three mother wavelets, namely Haar, Daubechies, and Coiflets were employed to decompose original time series data into different multi-frequency sub-signals at an appropriate decomposition level. Quantitative and qualitative performance evaluation criteria were used to select the best model for daily SSC estimation. The reliability of the developed models was also assessed using uncertainty analysis. Finally, it was revealed that the data pre-processing using wavelet transform improves the model’s predictive efficiency and reliability significantly. In this study, it was observed that the performance of the Coiflet wavelet-coupled ANFIS model is superior to other models and can be applied for daily SSC estimation of the highly dynamic rivers. As per sensitivity analysis, previous one-day SSC (St-1) is the most crucial input variable for daily SSC estimation of the Koyna River basin.

scholarly journals Implementation of IoT Framework with Data Analysis Using Deep Learning Methods for Occupancy Prediction in a Building

2021 ◽  
Vol 13 (3) ◽  
pp. 67
Author(s):  
Eric Hitimana ◽  
Gaurav Bajpai ◽  
Richard Musabe ◽  
Louis Sibomana ◽  
Jayavel Kayalvizhi
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Deep Learning ◽  
Time Series Data ◽  
Multivariate Time Series ◽  
Machine Learning Algorithms ◽  
Series Data ◽  
Support Vector ◽  
Human Beings ◽  
Feed Forward Network

Many countries worldwide face challenges in controlling building incidence prevention measures for fire disasters. The most critical issues are the localization, identification, detection of the room occupant. Internet of Things (IoT) along with machine learning proved the increase of the smartness of the building by providing real-time data acquisition using sensors and actuators for prediction mechanisms. This paper proposes the implementation of an IoT framework to capture indoor environmental parameters for occupancy multivariate time-series data. The application of the Long Short Term Memory (LSTM) Deep Learning algorithm is used to infer the knowledge of the presence of human beings. An experiment is conducted in an office room using multivariate time-series as predictors in the regression forecasting problem. The results obtained demonstrate that with the developed system it is possible to obtain, process, and store environmental information. The information collected was applied to the LSTM algorithm and compared with other machine learning algorithms. The compared algorithms are Support Vector Machine, Naïve Bayes Network, and Multilayer Perceptron Feed-Forward Network. The outcomes based on the parametric calibrations demonstrate that LSTM performs better in the context of the proposed application.

Prediction and Analysis of Gold Prices using Ensemble Machine Learning Algorithms

2021 ◽  
Vol 9 (VI) ◽  
pp. 4367-4374
Author(s):  
Gudipally Chandrashakar
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Time Series Data ◽  
Gold Price ◽  
Machine Learning Algorithms ◽  
Series Data ◽  
Gradient Boosting ◽  
Support Vector ◽  
Average Value ◽  
Ensemble Machine Learning

In this article, we used historical time series data up to the current day gold price. In this study of predicting gold price, we consider few correlating factors like silver price, copper price, standard, and poor’s 500 value, dollar-rupee exchange rate, Dow Jones Industrial Average Value. Considering the prices of every correlating factor and gold price data where dates ranging from 2008 January to 2021 February. Few algorithms of machine learning are used to analyze the time-series data are Random Forest Regression, Support Vector Regressor, Linear Regressor, ExtraTrees Regressor and Gradient boosting Regression. While seeing the results the Extra Tree Regressor algorithm gives the predicted value of gold prices more accurately.

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