Naive Support Vector Regression and Multilayer Perceptron benchmarks for the 2010 neural network grand competition (NNGC) on time series prediction

Crone, Sven F.; Kourentzes, Nikolaos

doi:10.1109/ijcnn.2010.5596636

Cited by 9 publications

(10 citation statements)

References 32 publications

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“…These models have been used successfully for modelling a broad range of time-series data [ 36 , 38 , 41 , 42 ]. The task of the artificial neural network is to model the underlying data-generating process during training so that valid forecasts can be made when the parameterized model is subsequently presented with new input data [ 35 ]. The most widely used and often preferred models when building artificial neural network forecasting models are those with a Multilayer Perceptron architecture, given its computational efficiency and efficacy and its ability to be extended to deep learning ( Figure 1 ) [ 1 , 35 , 36 , 38 , 41 , 43 , 44 ].…”

Section: Methodsmentioning

confidence: 99%

“…The task of the artificial neural network is to model the underlying data-generating process during training so that valid forecasts can be made when the parameterized model is subsequently presented with new input data [ 35 ]. The most widely used and often preferred models when building artificial neural network forecasting models are those with a Multilayer Perceptron architecture, given its computational efficiency and efficacy and its ability to be extended to deep learning ( Figure 1 ) [ 1 , 35 , 36 , 38 , 41 , 43 , 44 ]. Mathematically, a basic artificial neural network model (NNET) can be represented as follows:

Section: Methodsmentioning

confidence: 99%

“…The most widely used and often preferred models when building artificial neural network forecasting models are those with a Multilayer Perceptron architecture, given its computational efficiency and efficacy and its ability to be extended to deep learning ( Figure 1 ) [ 1 , 35 , 36 , 38 , 41 , 43 , 44 ]. Mathematically, a basic artificial neural network model (NNET) can be represented as follows:

in which there are two critical hyperparameters that need to be chosen, the embedding dimension, m , which captures the autocorrelation structure of the time series, and the number of hidden units, D [ 35 , 41 , 43 ]. For an artificial neural network model with a deep architecture (D-NNET), the number of hidden layers also needs to be determined [ 38 , 45 ].…”

Section: Methodsmentioning

confidence: 99%

“…An obvious prerequisite for adaptive model building is the ability for CL to be completed quickly. However, modern prediction models are inherently complex, often requiring hyperparameter optimization and tuning in high-dimensional parameter space [ 1 , 6 , 35 , 36 , 37 , 38 ]. Increasing the number of parameter weights, which is inextricably linked with model architecture complexity, also increases computation time [ 39 ].…”

Section: Related Workmentioning

confidence: 99%

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Implementing Gait Kinematic Trajectory Forecasting Models on an Embedded System

Shayne,

Molina,

et al. 2024

Sensors

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Smart algorithms for gait kinematic motion prediction in wearable assistive devices including prostheses, bionics, and exoskeletons can ensure safer and more effective device functionality. Although embedded systems can support the use of smart algorithms, there are important limitations associated with computational load. This poses a tangible barrier for models with increased complexity that demand substantial computational resources for superior performance. Forecasting through Recurrent Topology (FReT) represents a computationally lightweight time-series data forecasting algorithm with the ability to update and adapt to the input data structure that can predict complex dynamics. Here, we deployed FReT on an embedded system and evaluated its accuracy, computational time, and precision to forecast gait kinematics from lower-limb motion sensor data from fifteen subjects. FReT was compared to pretrained hyperparameter-optimized NNET and deep-NNET (D-NNET) model architectures, both with static model weight parameters and iteratively updated model weight parameters to enable adaptability to evolving data structures. We found that FReT was not only more accurate than all the network models, reducing the normalized root-mean-square error by almost half on average, but that it also provided the best balance between accuracy, computational time, and precision when considering the combination of these performance variables. The proposed FReT framework on an embedded system, with its improved performance, represents an important step towards the development of new sensor-aided technologies for assistive ambulatory devices.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

Implementing Gait Kinematic Trajectory Forecasting Models on an Embedded System

Shayne,

Molina,

et al. 2024

Sensors

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show abstract

“…Therefore, we present three novel 1 CI approaches for multi-step seasonal TSF: the Automatic Design of Artificial Neural Networks (ADANN), which uses genetic algorithms to evolve ANN structures; the SVM with time lag selection based on a sensitivity analysis procedure; and the linguistic fuzzy approach to the trend-cycle analysis and forecasts. The first two methods from different perspectives focus on feature and model selection process for CI methods that is often omitted [20]. The latter method focuses on the interpretability issue of fuzzy models.…”

Section: Introductionmentioning

confidence: 99%

Forecasting seasonal time series with computational intelligence: On recent methods and the potential of their combinations

Štěpnička

Cortez

Donate

et al. 2013

Expert Systems with Applications

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Accurate time series forecasting is a key issue to support individual and organizational decision making. In this paper, we introduce novel methods for multi-step seasonal time series forecasting. All the presented methods stem from computational intelligence techniques: evolutionary artificial neural networks, support vector machines and genuine linguistic fuzzy rules. Performance of the suggested methods is experimentally justified on seasonal time series from distinct domains on three forecasting horizons. The most important contribution is the introduction of a new hybrid combination using linguistic fuzzy rules and the other computational intelligence methods. This hybrid combination presents competitive forecasts, when compared with the popular ARIMA method. Moreover, such hybrid model is more easy to interpret by decision-makers when modeling trended series.

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Recurrent Multiplicative Neuron Model Artificial Neural Network for Non-linear Time Series Forecasting

Eğrioğlu

Yolcu

Aladağ

et al. 2014

Neural Process Lett

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Artificial neural networks (ANN) have been widely used in recent years to model non-linear time series since ANN approach is a responsive method and does not require some assumptions such as normality or linearity. An important problem with using ANN for time series forecasting is to determine the number of neurons in hidden layer. There have been some approaches in the literature to deal with the problem of determining the number of neurons in hidden layer. A new ANN model was suggested which is called multiplicative neuron model (MNM) in the literature. MNM has only one neuron in hidden layer. Therefore, the problem of determining the number of neurons in hidden layer is automatically solved when MNM is employed. Also, MNM can produce accurate forecasts for non-linear time series. ANN models utilized for non-linear time series have generally autoregressive structures since lagged variables of time series are generally inputs of these models. On the other hand, it is a wellknown fact that better forecasts for real life time series can be obtained from models whose inputs are lagged variables of error. In this study, a new recurrent multiplicative neuron neural network model is firstly proposed. In the proposed method, lagged variables of error are included in the model. Also, the problem of determining the number of neurons in hidden layer is avoided when the proposed method is used. To train the proposed neural network model, particle swarm optimization algorithm was used. To evaluate the performance of the proposed model, it was applied to a real life time series. Then, results produced by the proposed method were compared to those obtained from other methods. It was observed that the proposed method has superior performance to existing methods.

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Naive Support Vector Regression and Multilayer Perceptron benchmarks for the 2010 neural network grand competition (NNGC) on time series prediction

Cited by 9 publications

References 32 publications

Implementing Gait Kinematic Trajectory Forecasting Models on an Embedded System

Implementing Gait Kinematic Trajectory Forecasting Models on an Embedded System

Forecasting seasonal time series with computational intelligence: On recent methods and the potential of their combinations

Recurrent Multiplicative Neuron Model Artificial Neural Network for Non-linear Time Series Forecasting

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