A review and comparison of strategies for multi-step ahead time series forecasting based on the NN5 forecasting competition

Taieb, Souhaib Ben; Bontempi, Gianluca; Atiya, Amir F.; Sorjamaa, Antti

doi:10.1016/j.eswa.2012.01.039

Cited by 534 publications

(293 citation statements)

References 52 publications

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“…Strategies differ in how we estimate future values using the past values. There is no definite indication of superiority of one strategy over the others, as has been shown in [6], [7].…”

Section: Introductionmentioning

confidence: 98%

“…As it is clear from these names, in one-step-ahead prediction interest constitutes only estimation of the next single value ahead, while in the long-term prediction estimations of multiple future values are required. Quite often researchers address these problem separately [5], [6] since accumulation of errors and increasing uncertainties [7] make long-term prediction inherently more difficult problem. In this paper we consider long-term time series prediction.…”

Section: Introductionmentioning

confidence: 99%

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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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“…Strategies differ in how we estimate future values using the past values. There is no definite indication of superiority of one strategy over the others, as has been shown in [6], [7].…”

Section: Introductionmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

Long-term time series prediction using OP-ELM

et al. 2014

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“…Compared with one-step ahead forecasting, multi-step ahead forecasting can supply more information for electricity market participants. Since multi-step ahead forecasting has to deal with many other additional complications such as the accumulation of errors, increased uncertainty and reduced accuracy [35], it is therefore more difficult to obtain precise forecasting results. This paper aims to propose an ensemble model for electric load forecasting over different horizons.…”

Section: Data Description and Preprocessingmentioning

confidence: 99%

An Ensemble Model Based on Machine Learning Methods and Data Preprocessing for Short-Term Electric Load Forecasting

et al. 2017

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Abstract:The experience with deregulated electricity market has shown the increasingly important role of short-term electric load forecasting in the energy producing and scheduling. However, because of nonlinear, stochastic and nonstable characteristics associated with the electric load series, it is extremely difficult to precisely forecast the electric load. This paper aims to establish a novel ensemble model based on variational mode decomposition (VMD) and extreme learning machine (ELM) optimized by differential evolution (DE) algorithm for multi-step ahead electric load forecasting. The proposed model is novel in the sense that VMD is firstly applied to decompose the original electric load series into a set of components with different frequencies in order to effectively eliminate the stochastic fluctuation characteristic so as to improve the overall prediction accuracy. The proposed ensemble model is tested using two electric load series collected from New South Wales (NSW) and Queensland (QLD) in the Australian electricity market. The experimental results show that: (1) the data preprocessing by VMD can effectively decrease the stochastic fluctuation characteristics that existed in the electric load series, consequently improving the whole forecasting accuracy, and; (2) the proposed forecasting model performs better than all other benchmark models for both one-step and multi-step ahead electric load forecasting.

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“…There are five strategies for multi-step ahead forecasting, i.e. the recursive, direct, DirRec, MIMO and DIRMO (Taieb et al, 2012;.…”

Section: Time Series Forecasting In Hydrology and Beyondmentioning

confidence: 99%

“…We use the deseasonalized time series for the application of the forecasting methods, as suggested for the improvement of forecast quality (Taieb et al 2012;Hyndman and Athanasopoulos 2013).…”

Section: Real-world Time Seriesmentioning

confidence: 99%

Comparison of Stochastic and Machine Learning Methods for Multi-Step Ahead Forecasting of Hydrological Processes

Papacharalampous¹,

Tyralis²,

Koutsoyiannis³

2017

Preprint

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Abstract:We perform an extensive comparison between 11 stochastic to 9 machine learning methods regarding their multi-step ahead forecasting properties by conducting 12 large-scale computational experiments. Each of these experiments uses 2 000 time series generated by linear stationary stochastic processes. We conduct each simulation experiment twice; the first time using time series of 110 values and the second time using time series of 310 values. Additionally, we conduct 92 real-world case studies using mean monthly time series of streamflow and particularly focus on one of them to reinforce the findings and highlight important facts. We quantify the performance of the methods using 18 metrics. The results indicate that the machine learning methods do not differ dramatically from the stochastic, while none of the methods under comparison is uniformly better or worse than the rest. However, there are methods that are regularly better or worse than others according to specific metrics.

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A review and comparison of strategies for multi-step ahead time series forecasting based on the NN5 forecasting competition

Cited by 534 publications

References 52 publications

Long-term time series prediction using OP-ELM

Long-term time series prediction using OP-ELM

An Ensemble Model Based on Machine Learning Methods and Data Preprocessing for Short-Term Electric Load Forecasting

Comparison of Stochastic and Machine Learning Methods for Multi-Step Ahead Forecasting of Hydrological Processes

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