Multivariate Prediction of Energy Time Series by Autoencoded LSTM Networks

2021 ◽  
Author(s):  
Federico Succetti ◽  
Francesco Di Luzio ◽  
Andrea Ceschini ◽  
Antonello Rosato ◽  
Rodolfo Araneo ◽  
...  
Keyword(s):  
Time Series ◽  
Multivariate Prediction

scholarly journals 2-D Convolutional Deep Neural Network for the Multivariate Prediction of Photovoltaic Time Series

Energies ◽  
2021 ◽  
Vol 14 (9) ◽  
pp. 2392
Author(s):  
Antonello Rosato ◽  
Rodolfo Araneo ◽  
Amedeo Andreotti ◽  
Federico Succetti ◽  
Massimo Panella
Keyword(s):  
Time Series ◽  
Short Term Memory ◽  
Time Series Prediction ◽  
Short Term ◽  
Neural Architecture ◽  
Network Layers ◽  
Learning Scheme ◽  
World Energy ◽  
Multivariate Prediction ◽  
Long Short Term Memory

Here, we propose a new deep learning scheme to solve the energy time series prediction problem. The model implementation is based on the use of Long Short-Term Memory networks and Convolutional Neural Networks. These techniques are combined in such a fashion that inter-dependencies among several different time series can be exploited and used for forecasting purposes by filtering and joining their samples. The resulting learning scheme can be summarized as a superposition of network layers, resulting in a stacked deep neural architecture. We proved the accuracy and robustness of the proposed approach by testing it on real-world energy problems.

scholarly journals Deep Neural Networks for Multivariate Prediction of Photovoltaic Power Time Series

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 211490-211505
Author(s):  
Federico Succetti ◽  
Antonello Rosato ◽  
Rodolfo Araneo ◽  
Massimo Panella
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Deep Neural Networks ◽  
Photovoltaic Power ◽  
Multivariate Prediction

scholarly journals A Multi-Method Survey on the Use of Sentiment Analysis in Multivariate Financial Time Series Forecasting

Entropy ◽  
2021 ◽  
Vol 23 (12) ◽  
pp. 1603
Author(s):  
Charalampos M. Liapis ◽  
Aikaterini Karanikola ◽  
Sotiris Kotsiantis
Keyword(s):  
Time Series ◽  
Sentiment Analysis ◽  
Short Term Memory ◽  
Financial Time Series ◽  
Time Series Forecasting ◽  
Financial Time ◽  
Experimental Process ◽  
Multivariate Prediction ◽  
The One ◽  
Practice Time

In practice, time series forecasting involves the creation of models that generalize data from past values and produce future predictions. Moreover, regarding financial time series forecasting, it can be assumed that the procedure involves phenomena partly shaped by the social environment. Thus, the present work is concerned with the study of the use of sentiment analysis methods in data extracted from social networks and their utilization in multivariate prediction architectures that involve financial data. Through an extensive experimental process, 22 different input setups using such extracted information were tested, over a total of 16 different datasets, under the schemes of 27 different algorithms. The comparisons were structured under two case studies. The first concerns possible improvements in the performance of the forecasts in light of the use of sentiment analysis systems in time series forecasting. The second, having as a framework all the possible versions of the above configuration, concerns the selection of the methods that perform best. The results, as presented by various illustrations, indicate, on the one hand, the conditional improvement of predictability after the use of specific sentiment setups in long-term forecasts and, on the other, a universal predominance of long short-term memory architectures.

scholarly journals A scalable framework for large time series prediction

2021 ◽  
Vol 63 (5) ◽  
pp. 1093-1116
Author(s):  
Youssef Hmamouche ◽  
Lotfi Lakhal ◽  
Alain Casali
Keyword(s):  
Time Series ◽  
Large Time ◽  
Prediction Models ◽  
Search Algorithm ◽  
Time Series Prediction ◽  
Large Data ◽  
Filter Method ◽  
Selection Algorithm ◽  
Feature Selection Algorithm ◽  
Multivariate Prediction

AbstractKnowledge discovery systems are nowadays supposed to store and process very large data. When working with big time series, multivariate prediction becomes more and more complicated because the use of all the variables does not allow to have the most accurate predictions and poses certain problems for classical prediction models. In this article, we present a scalable prediction process for large time series prediction, including a new algorithm for identifying time series predictors, which analyses the dependencies between time series using the mutual reinforcement principle between Hubs and Authorities of the Hits (Hyperlink-Induced Topic Search) algorithm. The proposed framework is evaluated on 3 real datasets. The results show that the best predictions are obtained using a very small number of predictors compared to the initial number of variables. The proposed feature selection algorithm shows promising results compared to widely known algorithms, such as the classic and the kernel principle component analysis, factor analysis, and the fast correlation-based filter method, and improves the prediction accuracy of many time series of the used datasets.

The LDE-Type Flare Occurrence (1969-1993)

1994 ◽  
Vol 144 ◽  
pp. 279-282
Author(s):  
A. Antalová
Keyword(s):  
Time Series ◽  
Fourier Analysis ◽  
Sunspot Cycle ◽  
List Type ◽  
Type Number ◽  
Solar Cycles ◽  
Type Iv ◽  
Long Duration ◽  
Cycle Maximum ◽  
Flare Index

AbstractThe occurrence of LDE-type flares in the last three cycles has been investigated. The Fourier analysis spectrum was calculated for the time series of the LDE-type flare occurrence during the 20-th, the 21-st and the rising part of the 22-nd cycle. LDE-type flares (Long Duration Events in SXR) are associated with the interplanetary protons (SEP and STIP as well), energized coronal archs and radio type IV emission. Generally, in all the cycles considered, LDE-type flares mainly originated during a 6-year interval of the respective cycle (2 years before and 4 years after the sunspot cycle maximum). The following significant periodicities were found:• in the 20-th cycle: 1.4, 2.1, 2.9, 4.0, 10.7 and 54.2 of month,• in the 21-st cycle: 1.2, 1.6, 2.8, 4.9, 7.8 and 44.5 of month,• in the 22-nd cycle, till March 1992: 1.4, 1.8, 2.4, 7.2, 8.7, 11.8 and 29.1 of month,• in all interval (1969-1992):a)the longer periodicities: 232.1, 121.1 (the dominant at 10.1 of year), 80.7, 61.9 and 25.6 of month,b)the shorter periodicities: 4.7, 5.0, 6.8, 7.9, 9.1, 15.8 and 20.4 of month.Fourier analysis of the LDE-type flare index (FI) yields significant peaks at 2.3 - 2.9 months and 4.2 - 4.9 months. These short periodicities correspond remarkably in the all three last solar cycles. The larger periodicities are different in respective cycles.

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