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A Novel Hybrid Algorithm to Forecast Functional Time Series Based on Pattern Sequence Similarity with Application to Electricity Demand

机译：一种新的混合算法，以基于模式序列相似性与电力需求的模式序列相似性预测功能时间序列

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Francisco Martínez-Álvarez; Amandine Schmutz; Gualberto Asencio-Cortés; Julien Jacques;
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年度 2018
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1. Big data time series forecasting based on pattern sequence similarity and its application to the electricity demand [J] . Perez-Chacon R., Asencio-Cortes G., Martinez-Alvarez F., Information Sciences: An International Journal . 2020,第1期

机译：基于模式序列相似性的大数据时间序列预测及其对电力需求的应用
2. Modeling a combined forecast algorithm based on sequence patterns and near characteristics: An application for tourism demand forecasting [J] . Wang Jun, Luo Yuyan, Tang Lingyu, Chaos, Solitons and Fractals: Applications in Science and Engineering: An Interdisciplinary Journal of Nonlinear Science . 2018,第期

机译：基于序列模式和近的特征建模组合预测算法：旅游需求预测申请
3. Energy Time Series Forecasting Based on Pattern Sequence Similarity [J] . Martinez Alvarez Francisco, Troncoso Alicia, Riquelme Jose C., Knowledge and Data Engineering, IEEE Transactions on . 2011,第8期

机译：基于模式序列相似度的能源时间序列预测
4. Combining pattern sequence similarity with neural networks for forecasting electricity demand time series [C] . Koprinska Irena, Rana Mashud, Troncoso Alicia, International Joint Conference on Neural Networks . 2013

机译：将模式序列相似性与神经网络相结合以预测电力需求时间序列
5. New Tools for Econometric Analysis of High-Frequency Time Series Data - Application to Demand-Side Management in Electricity Markets. [D] . Balandat, Maximilian. 2016

机译：高频时间序列数据计量经济学的新工具-在电力市场需求侧管理中的应用。
6. A Hybrid Model for Forecasting Sunspots Time Series Based on Variational Mode Decomposition and Backpropagation Neural Network Improved by Firefly Algorithm [O] . Guohui Li, Xiao Ma, Hong Yang 2018

机译：基于萤火虫算法的变分分解与BP神经网络的太阳黑子时间序列混合预测模型
7. Figure 4: (A) One conserved sequence, which occurs 79 times in 46,264 binding site peaks from the ChIP-seq data-set. The mutation profile of this conserved sequence is illustrated, where ’_ ’ indicates this base is unchanged; DEL indicates this base is lost; INS X indicates a new base X is inserted in front of this base. (B) Several repeated elements patterns are listed. (C) In the first column, the top five DNA motifs, mined by meme-chip tools (Machanick Bailey, 2011) are illustrated. The resemblant conserved sequences, found by the CFSP algorithm are listed in the second column. In the third column, the position-specific scoring matrices, which are transformed from mutational information are listed. The similarity between meme motif and resemblant conserved sequence with PSSM format was calculated via a stamp motif comparison tool (Mahony Benos, 2007). The E-values for the similarity of those pairs is displayed in the fourth column. (D) One motif is selected in each group clustered by gkmsvm descriptors, and the corresponding motif found by the CFSP algorithm is listed below. (E) There are additional datasets (File No: ENCFF100GRL, ENCFF616IRT, ENCFF870CER, Target: SREBF1) collected from https://www.encodeproject.org. The top two motifs are selected in each file using meme tools, and the corresponding motifs found by our algorithm are listed below. [O] . -1

机译：图4：（a）一种保守序列，其发生在芯片-SEQ数据集中的46,264个结合位点峰值中的79倍。说明了这种保守序列的突变分布，其中'_'表示该碱度不变; del表示此基础丢失; INS X表示新的基础X插入此基础前面。（b）列出了几种重复的元素模式。（c）在第一栏中，示出了由MEME芯片工具（Machanick＆Bailey，2011）开采的前五个DNA主题。由CFSP算法发现的相应保守序列列于第二列中。在第三列中，列出了从突变信息转换的特定位置的评分矩阵。 MEME主题与PSSM格式的相似性与PSSM格式之间的相似性通过邮票图章比较工具（Mahony＆Benos，2007）计算。这些对相似性的电子值显示在第四列中。（d）在由GKMSVM描述符聚集的每个组中选择了一个图案，下面列出了CFSP算法的相应主题。（e）从https://www.encodeproject.org收集的，有附加数据集（文件no：cernff100grl，cenf616irl，conf8.20cer，target：srebf1）。使用MEME工具在每个文件中选择前两个图案，并且我们的算法发现的相应主题如下所示。

A Novel Hybrid Algorithm to Forecast Functional Time Series Based on Pattern Sequence Similarity with Application to Electricity Demand

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