PSF: Introduction to R Package for Pattern Sequence Based Forecasting Algorithm

Bokde, Neeraj Dhanraj; Asencio-Cortés, Gualberto; Martínez‐Álvarez, Francisco; Kulat, K. D.

doi:10.32614/rj-2017-021

Cited by 34 publications

(24 citation statements)

References 13 publications

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“…Recently, Martínez-Álvarez et al [10] indicate the importance of pattern sequence similarity, and introduce the pattern sequence-based forecasting (PSF) algorithm, which contains clustering (selection of the optimum number of clusters) and prediction (like optimum window size selection for specific patterns and prediction of future values). Later, Bokde et al [11] published the R code for modeling.…”

Section: Introductionmentioning

confidence: 99%

Application of the Weighted K-Nearest Neighbor Algorithm for Short-Term Load Forecasting

Fan¹,

Guo²,

Zheng³

et al. 2019

Energies

104

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In this paper, the historical power load data from the National Electricity Market (Australia) is used to analyze the characteristics and regulations of electricity (the average value of every eight hours). Then, considering the inverse of Euclidean distance as the weight, this paper proposes a novel short-term load forecasting model based on the weighted k-nearest neighbor algorithm to receive higher satisfied accuracy. In addition, the forecasting errors are compared with the back-propagation neural network model and the autoregressive moving average model. The comparison results demonstrate that the proposed forecasting model could reflect variation trend and has good fitting ability in short-term load forecasting.

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Section: Introductionmentioning

confidence: 99%

Application of the Weighted K-Nearest Neighbor Algorithm for Short-Term Load Forecasting

Fan¹,

Guo²,

Zheng³

et al. 2019

Energies

104

View full text Add to dashboard Cite

show abstract

“…In this policy, the cluster size is finalized with the use of multiple statistical tests to ensure efficiency in the clustering process. Further, References [18][19][20] used a single index (Silhouette index [15]) to simplify computation complexity in the clustering process. Then, with respect to cluster heads (K) generated with the k-means clustering method, the values in the original time series are transformed into label series.…”

Section: Conventional Psf Methodologymentioning

confidence: 99%

“…However, in order to predict future values for multiple time indices, the current predicted value is appended to the original time series, and this procedure continues until the desired number of prediction values are obtained. The usability and superior performance of the PSF method for distinct univariate time-series prediction applications are discussed in References [20][21][22][23][24].…”

Section: Conventional Psf Methodologymentioning

confidence: 99%

A Novel and Alternative Approach for Direct and Indirect Wind-Power Prediction Methods

et al. 2018

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Wind energy is a variable energy source with a growing presence in many electrical networks across the world. Wind-speed prediction has become an important tool for many agents involved in energy markets. In this paper, an approach to this problem is proposed by means of a novel method that outperforms results obtained by current direct and indirect wind-power prediction procedures. The first difference is that it is not strictly a direct or indirect method in the conventional sense because it uses information from both wind-speed and wind-power data series to obtain a wind-power series. The second difference is that it smooths down the wind-power series obtained in the first stage, and uses the resulting series for predicting new wind-power values. The process of smoothing is based on the label sequence generation process discussed in the pattern sequence forecasting algorithm and the Naive Bayesian method-based matching process. The result is a less chaotic way to predict wind speed than those offered by other existing methods. It has been assessed in multiple simulations, for which three different error measures have been used.

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“…However, the performance of statistical methods gets degraded for long-term forecasting. In statistical methods, the various methods used for prediction are ARMA [3,68], ARIMA [69], Pattern Sequence-based forecasting (PSF) method [70][71][72], Kalman filters [73], model based approaches [74], Particle Swarm Optimization [75] and many others. However, Refs.…”

Section: Conventional Models For Wind Data Predictionmentioning

confidence: 99%

“…The PSF method uses labels for different patterns existing in time series data, instead of the original time series data. The detailed methodology of PSF method is discussed in [70,127]. Ref.…”

Section: Emd-knn Modelmentioning

confidence: 99%

A Review on Hybrid Empirical Mode Decomposition Models for Wind Speed and Wind Power Prediction

et al. 2019

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Reliable and accurate planning and scheduling of wind farms and power grids to ensure sustainable use of wind energy can be better achieved with the use of precise and accurate prediction models. However, due to the highly chaotic, intermittent and stochastic behavior of wind, which means a high level of difficulty when predicting wind speed and, consequently, wind power, the evolution of models capable of narrating data of such a complexity is an emerging area of research. A thorough review of literature, present research overviews, and information about possible expansions and extensions of models play a significant role in the enhancement of the potential of accurate prediction models. The last few decades have experienced a remarkable breakthrough in the development of accurate prediction models. Among various physical, statistical and artificial intelligent models developed over this period, the models hybridized with pre-processing or/and post-processing methods have seen promising prediction results in wind applications. The present review is focused on hybrid empirical mode decomposition (EMD) or ensemble empirical mode decomposition (EEMD) models with their advantages, timely growth and possible future in wind speed and power forecasting. Over the years, the practice of EEMD based hybrid models in wind data predictions has risen steadily and has become popular because of the robust and accurate nature of this approach. In addition, this review is focused on distinct attributes including the evolution of EMD based methods, novel techniques of treating Intrinsic Mode Functions (IMFs) generated with EMD/EEMD and overview of suitable error measures for such studies.

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PSF: Introduction to R Package for Pattern Sequence Based Forecasting Algorithm

Cited by 34 publications

References 13 publications

Application of the Weighted K-Nearest Neighbor Algorithm for Short-Term Load Forecasting

Application of the Weighted K-Nearest Neighbor Algorithm for Short-Term Load Forecasting

A Novel and Alternative Approach for Direct and Indirect Wind-Power Prediction Methods

A Review on Hybrid Empirical Mode Decomposition Models for Wind Speed and Wind Power Prediction

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