Advances in forecasting with neural networks? Empirical evidence from the NN3 competition on time series prediction

Crone, Sven F.; Hibon, Michèle; Νικολόπουλος, Κωνσταντίνος

doi:10.1016/j.ijforecast.2011.04.001

Cited by 229 publications

(123 citation statements)

References 57 publications

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“…There are situations where intensive human involvement or large computational time is not affordable. Neither we nor other authors claim that neural networks are generally better method than classical statistical methods, but definitely they and other computational intelligence methods have shown its viability [4].…”

Section: Introductionmentioning

confidence: 78%

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Long-term time series prediction using OP-ELM

et al. 2014

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In this paper, an Optimally Pruned Extreme Learning Machine (OP-ELM) is applied to the problem of long-term time series prediction. Three known strategies for the long-term time series prediction i.e. Recursive, Direct and DirRec are considered in combination with OP-ELM and compared with a baseline linear least squares model and Least-Squares Support Vector Machines (LS-SVM). Among these three strategies DirRec is the most time consuming and its usage with nonlinear models like LS-SVM, where several hyperparameters need to be adjusted, leads to relatively heavy computations. It is shown that OP-ELM, being also a nonlinear model, allows reasonable computational time for the DirRec strategy. In all our experiments, except one, OP-ELM with DirRec strategy outperforms linear model with any strategy. In contrast to the proposed algorithm, LS-SVM behaves unstably without variable selection. It is also shown that there are no superior strategy for OP-ELM: any of three can be the best. In addition, prediction accuracy of ensemble of OP-ELM is studied and it is shown that averaging predictions of ensemble can improve the accuracy (Mean Square Error) dramatically.

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Section: Introductionmentioning

confidence: 78%

“…Neural network (NN) methods have attracted significant attention for time series prediction problems [4]. NNs are general nonlinear regression technique which can be applied to time series.…”

Section: Introductionmentioning

confidence: 99%

Long-term time series prediction using OP-ELM

et al. 2014

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“…The measurable results on these series are presented in Table 4, Table 5 and Table 6 which show the performance of the system according to SMAPE metrics [37,38] applied to each series validation and test sets, averaged across short and long forecast horizons, for time series categorized as long and short [34] using different forecasting methods, such as EAS, NNMod. and ARMA.…”

Section: Interpretation Of the Resultsmentioning

confidence: 99%

“…We select some time series data from the NN3 competition [34]. The complete dataset of 111 time series of the NN3 dataset was chosen containing between 68 and 144 observations.…”

Section: Benchmark Chaotic Time Seriesmentioning

confidence: 99%

Energy Associated Tuning Method for Short-Term Series Forecasting by Complete and Incomplete Datasets

Rivero

Pucheta

Laboret

et al. 2016

Journal of Artificial Intelligence and Soft Computing Research

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This article presents short-term predictions using neural networks tuned by energy associated to series based-predictor filter for complete and incomplete datasets. A benchmark of high roughness time series from Mackay Glass (MG), Logistic (LOG), Henon (HEN) and some univariate series chosen from NN3 Forecasting Competition are used. An average smoothing technique is assumed to complete the data missing in the dataset. The Hurst parameter estimated through wavelets is used to estimate the roughness of the real and forecasted series. The validation and horizon of the time series is presented by the 15 values ahead. The performance of the proposed filter shows that even a short dataset is incomplete, besides a linear smoothing technique employed; the prediction is almost fair by means of SMAPE index. Although the major result shows that the predictor system based on energy associated to series has an optimal performance from several chaotic time series, in particular, this method among other provides a good estimation when the short-term series are taken from one point observations.

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“…The main advantage of an SVM method is that that the solution is unique and there are no risk to move towards local minima, but some problems remain as the choice of the kernel parameters which influences the structure of the feature space, affecting the final solution. Another method is based on an Artificial Neural Network (ANN) [8,[15][16][17]. The most widely used ANN architectures for forecasting problems are given by multi-layer Feed Forward Network (FNN) architectures [18,19], where the input nodes are given by the successive observations of the time series; that is, target y t is a function of the values y t−1 , y t−2 , .…”

Section: Introductionmentioning

confidence: 99%

Time Series Seasonal Analysis Based on Fuzzy Transforms

Martino

Sessa

2017

Symmetry

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Abstract:We define a new seasonal forecasting method based on fuzzy transforms. We use the best interpolating polynomial for extracting the trend of the time series and generate the inverse fuzzy transform on each seasonal subset of the universe of discourse for predicting the value of an assigned output. In the first example, we use the daily weather dataset of the municipality of Naples (Italy) starting from data collected from 2003 to 2015 making predictions on mean temperature, max temperature and min temperature, all considered daily. In the second example, we use the daily mean temperature measured at the weather station "Chiavari Caperana" in the Liguria Italian Region. We compare the results with our method, the average seasonal variation, Auto Regressive Integrated Moving Average (ARIMA) and the usual fuzzy transforms concluding that the best results are obtained under our approach in both examples. In addition, the comparison results show that, for seasonal time series that have no consistent irregular variations, the performance obtained with our method is comparable with the ones obtained using Support Vector Machine-and Artificial Neural Networks-based models.

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Advances in forecasting with neural networks? Empirical evidence from the NN3 competition on time series prediction

Cited by 229 publications

References 57 publications

Long-term time series prediction using OP-ELM

Long-term time series prediction using OP-ELM

Energy Associated Tuning Method for Short-Term Series Forecasting by Complete and Incomplete Datasets

Time Series Seasonal Analysis Based on Fuzzy Transforms

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