scholarly journals A Computational Method for Predicting the Entropy of Energy Market Time Series

2016 ◽  
pp. 39-44
Author(s):  
Francesco Benedetto ◽  
Gaetano Giunta ◽  
Loretta Mastroeni
Keyword(s):  
Time Series ◽  
Computational Method ◽  
Energy Market

scholarly journals A TIME-SERIES AND SPATIAL ANALYSIS OF 56 YEARS (1961–2017) OF RAINFALL HISTORICAL DATA FROM MALAYBALAY, BUKIDNON

2021 ◽  
Vol XLVI-4/W6-2021 ◽  
pp. 71-76
Author(s):  
B. J. Buenaobra ◽  
M. K. L. Alleto ◽  
J. M. V. Manhuyod
Keyword(s):  
Time Series ◽  
Spatial Analysis ◽  
Null Hypothesis ◽  
Historical Data ◽  
Rainfall Data ◽  
Computational Method ◽  
P Value ◽  
Time Domain Signal ◽  
Augmented Dickey Fuller ◽  
Stationarity Test

Abstract. This paper focuses on using time series and spatial analysis methods to detect climate change indicators in Malaybalay, Bukidnon. We look at 56 years of historical rainfall data between the years 1961 to 2017 and perform a computational method for data processing to arrive at spatial statistics and provide data visualization. We demonstrate the use of the Augmented Dickey-Fuller test (ADF), where a p-value is tested versus a threshold to reject or accept the null hypothesis for a stationarity test. For the seasonality test, we perform a time-domain signal processing by an autocorrelation function. The time-series analysis shows that for Malaybalay, Bukidnon rainfall data shows ADF statistic of −16.348964, a p-value = 0.000000 with critical values 1%:−3.431, 5%:−2.862, 10%:−2.567. Hence, the significant negative values indicate more likely to reject the null hypothesis. We showed that rainfall does not demonstrate periodicity, is not seasonal, and is non-stationary. This work does not cover those that can be detected and attributed to anthropogenic causes.

Time series computational prediction of vaccines for influenza A H3N2 with recurrent neural networks

2020 ◽  
Vol 18 (01) ◽  
pp. 2040002 ◽  
Author(s):  
Rui Yin ◽  
Yu Zhang ◽  
Xinrui Zhou ◽  
Chee Keong Kwoh
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Recurrent Neural Networks ◽  
Influenza A ◽  
Viral Evolution ◽  
Computational Prediction ◽  
Single Step ◽  
Influenza Viruses ◽  
Computational Method ◽  
Influenza Vaccines

Influenza viruses are persistently threatening public health, causing annual epidemics and sporadic pandemics due to rapid viral evolution. Vaccines are used to prevent influenza infections but the composition of the influenza vaccines have to be updated regularly to ensure its efficacy. Computational tools and analyses have become increasingly important in guiding the process of vaccine selection. By constructing time-series training samples with splittings and embeddings, we develop a computational method for predicting suitable strains as the recommendation of the influenza vaccines using recurrent neural networks (RNNs). The Encoder-decoder architecture of RNN model enables us to perform sequence-to-sequence prediction. We employ this model to predict the prevalent sequence of the H3N2 viruses sampled from 2006 to 2017. The identity between our predicted sequence and recommended vaccines is greater than 98% and the [Formula: see text] indicates their antigenic similarity. The multi-step vaccine prediction further demonstrates the robustness of our method which achieves comparable results in contrast to single step prediction. The results show significant matches of the recommended vaccine strains to the circulating strains. We believe it would facilitate the process of vaccine selection and surveillance of seasonal influenza epidemics.

scholarly journals Parallel Processing of Remote Sensing Time Series Applied to Land-Use and Land-Cover Classification

2021 ◽  
Vol 12 (4) ◽  
Author(s):  
Roberto U. Paiva ◽  
Sávio S. T. Oliveira ◽  
Luiz M. L. Pascoal ◽  
Leandro L. Parente ◽  
Wellington S. Martins
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Time Series ◽  
Land Cover ◽  
Execution Time ◽  
Similarity Measures ◽  
Remote Sensing Data ◽  
Computational Method ◽  
Classification Models ◽  
Sensing Time

The increase in satellite launches into Earth's orbit in recent years has generated a huge amount of remote sensing data. These data, in the form of time series, have been used in automated classification approaches, generating land-use and land-cover (LULC) products for different landscapes around the world. Dynamic Time Warping (DTW) is a well-known computational method used to measure the similarity between time series. Tt has been used in many algorithms for remote sensing time series analysis. These DTW-based algorithms are capable of generating similarity measures between time series and patterns. These measures can be used as meta-features to increase the accuracy results of classification models. However, DTW-based algorithms require a lot of computational resources and have a high execution time, which makes them difficult to use in large volumes of data. This article presents a parallel and fully scalable solution to optimize the construction of meta-features through remote sensing time series (RSTS). In addition, results of the application of the generated meta-features in the training and evaluation of classification models using Random Forest are presented. The results show that the proposed approaches have led to improvements in execution time and accuracy when compared to traditional strategies.

scholarly journals Critical Transitions in Intensive Care Units: A Sepsis Case Study

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Pejman F. Ghalati ◽  
Satya S. Samal ◽  
Jayesh S. Bhat ◽  
Robert Deisz ◽  
Gernot Marx ◽  
...  
Keyword(s):  
Septic Shock ◽  
Time Series ◽  
Early Warning ◽  
Moving Average ◽  
Sampling Strategy ◽  
Underlying Disease ◽  
Computational Method ◽  
Data Sampling ◽  
Critical Transition ◽  
Critical Transitions

Abstract The progression of complex human diseases is associated with critical transitions across dynamical regimes. These transitions often spawn early-warning signals and provide insights into the underlying disease-driving mechanisms. In this paper, we propose a computational method based on surprise loss (SL) to discover data-driven indicators of such transitions in a multivariate time series dataset of septic shock and non-sepsis patient cohorts (MIMIC-III database). The core idea of SL is to train a mathematical model on time series in an unsupervised fashion and to quantify the deterioration of the model’s forecast (out-of-sample) performance relative to its past (in-sample) performance. Considering the highest value of the moving average of SL as a critical transition, our retrospective analysis revealed that critical transitions occurred at a median of over 35 hours before the onset of septic shock, which suggests the applicability of our method as an early-warning indicator. Furthermore, we show that clinical variables at critical-transition regions are significantly different between septic shock and non-sepsis cohorts. Therefore, our paper contributes a critical-transition-based data-sampling strategy that can be utilized for further analysis, such as patient classification. Moreover, our method outperformed other indicators of critical transition in complex systems, such as temporal autocorrelation and variance.

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