scholarly journals The geoengineering approach to the study of rivers and reservoirs

2019 ◽  
Vol 488 (1) ◽  
pp. 1-13
Author(s):  
Patrick W. M. Corbett ◽  
Amanda Owen ◽  
Adrian J. Hartley ◽  
Sila Pla-Pueyo ◽  
Daniel Barreto ◽  
...  
Keyword(s):  
Time Series ◽  
Statistical Techniques ◽  
Government Agencies ◽  
Short Term ◽  
Geological Record ◽  
Global Sustainability ◽  
Spatial Sense ◽  
The Usa ◽  
Impacts Of Human Activity ◽  
Rivers And Reservoirs

AbstractThis Special Publication contains contributions for two meetings held to explore the links between geoscience and engineering in rivers and reservoirs (surface and subsurface). The first meeting was held in Brazil and, as a result, the volume contains many contributions from Brazil. The second was held in Edinburgh, and produced contributions from modern rivers in the USA, China, India and Scotland. The geological record from Carboniferous to Recent is represented. A range of outcrop techniques are presented along with statistical techniques used to identify patterns in the time series and spatial sense. The book is intended to cover the cross-disciplinary interest in rivers and their sediments, and will interest geologists, geomorphologists, civil, geotechnical and petroleum engineers, and government agencies. Some of the papers collected here demonstrate longer term impacts of human activity on rivers and how these might change the future geological record and, more importantly in the short term, impact on the UN Global Sustainability Goals.

scholarly journals Estimating eigenvalues of dynamical systems from time series with applications to predicting cardiac alternans

2012 ◽  
Vol 468 (2147) ◽  
pp. 3649-3666 ◽  
Author(s):  
Adam Petrie ◽  
Xiaopeng Zhao
Keyword(s):  
Dynamical System ◽  
Time Series ◽  
Data Driven ◽  
Standard Errors ◽  
Statistical Techniques ◽  
Likelihood Methods ◽  
Maximum Likelihood Methods ◽  
The Usa ◽  
Cardiac Alternans ◽  
The Stability

The stability of a dynamical system can be indicated by eigenvalues of its underlying mathematical model. However, eigenvalue analysis of a complicated system (e.g. the heart) may be extremely difficult because full models may be intractable or unavailable. We develop data-driven statistical techniques, which are independent of any underlying dynamical model, that use principal components and maximum-likelihood methods to estimate the dominant eigenvalues and their standard errors from the time series of one or a few measurable quantities, e.g. transmembrane voltages in cardiac experiments. The techniques are applied to predicting cardiac alternans that is characterized by an eigenvalue approaching −1. Cardiac alternans signals a vulnerability to ventricular fibrillation, the leading cause of death in the USA.

scholarly journals Analisis ekspor kopi Indonesia

2017 ◽  
Vol 12 (1) ◽  
pp. 1-10
Author(s):  
Rexsi Nopriyandi ◽  
Haryadi Haryadi
Keyword(s):  
Time Series ◽  
Exchange Rate ◽  
Exchange Rates ◽  
Time Series Data ◽  
Series Data ◽  
Government Agencies ◽  
Short Term ◽  
Correction Model ◽  
Coffee Price

This study aims to analyze the factors that influence Indonesian coffee exports. The data in this study is time series data, which were obtained from various government agencies. The Error Correction Model (ECM) method is used to analyze the effect of coffee prices, GDP and the exchange rate on the volume of Indonesian coffee exports. The estimation results find that coffee prices, Indonesian GDP and exchange rates have a short-term relationship and a long-term balance of the volume of coffee exports. Based on the long-term estimation of the coffee price variable, GDP and exchange rates do not significantly affect the volume of coffee exports, while in the short term these three variables influence the volume of coffee exports

scholarly journals Analisis cadangan devisa Indonesia dan faktor-faktor yang mempengaruhinya

2017 ◽  
Vol 12 (1) ◽  
pp. 25-30
Author(s):  
Pundy Sayoga ◽  
Syamsurijal Tan
Keyword(s):  
Time Series ◽  
Exchange Rate ◽  
Exchange Rates ◽  
Time Series Data ◽  
Series Data ◽  
Government Agencies ◽  
Short Term ◽  
Correction Model ◽  
Coffee Price

This study aims to analyze the factors that influence Indonesian coffee exports. The data in this study is time series data, which were obtained from various government agencies. The Error Correction Model (ECM) method is used to analyze the effect of coffee prices, GDP and the exchange rate on the volume of Indonesian coffee exports. The estimation results find that coffee prices, Indonesian GDP and exchange rates have a short-term relationship and a long-term balance of the volume of coffee exports. Based on the long-term estimation of the coffee price variable, GDP and exchange rates do not significantly affect the volume of coffee exports, while in the short term these three variables influence the volume of coffee exports.

scholarly journals Short-term household load forecasting based on Long- and Short-term Time-series network

2021 ◽  
Vol 7 ◽  
pp. 58-64
Author(s):  
Xifeng Guo ◽  
Ye Gao ◽  
Yupeng Li ◽  
Di Zheng ◽  
Dan Shan
Keyword(s):  
Time Series ◽  
Load Forecasting ◽  
Short Term

scholarly journals Long-Term Data Traffic Forecasting for Network Dimensioning in LTE with Short Time Series

Electronics ◽  
2021 ◽  
Vol 10 (10) ◽  
pp. 1151
Author(s):  
Carolina Gijón ◽  
Matías Toril ◽  
Salvador Luna-Ramírez ◽  
María Luisa Marí-Altozano ◽  
José María Ruiz-Avilés
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Time Series Analysis ◽  
Data Traffic ◽  
Medium Term ◽  
Short Term ◽  
Traffic Forecasting ◽  
Series Analysis ◽  
Radio Planning

Network dimensioning is a critical task in current mobile networks, as any failure in this process leads to degraded user experience or unnecessary upgrades of network resources. For this purpose, radio planning tools often predict monthly busy-hour data traffic to detect capacity bottlenecks in advance. Supervised Learning (SL) arises as a promising solution to improve predictions obtained with legacy approaches. Previous works have shown that deep learning outperforms classical time series analysis when predicting data traffic in cellular networks in the short term (seconds/minutes) and medium term (hours/days) from long historical data series. However, long-term forecasting (several months horizon) performed in radio planning tools relies on short and noisy time series, thus requiring a separate analysis. In this work, we present the first study comparing SL and time series analysis approaches to predict monthly busy-hour data traffic on a cell basis in a live LTE network. To this end, an extensive dataset is collected, comprising data traffic per cell for a whole country during 30 months. The considered methods include Random Forest, different Neural Networks, Support Vector Regression, Seasonal Auto Regressive Integrated Moving Average and Additive Holt–Winters. Results show that SL models outperform time series approaches, while reducing data storage capacity requirements. More importantly, unlike in short-term and medium-term traffic forecasting, non-deep SL approaches are competitive with deep learning while being more computationally efficient.

scholarly journals A Bayesian Model to Forecast the Time Series Kinetic Energy Data for a Power System

Energies ◽  
2021 ◽  
Vol 14 (11) ◽  
pp. 3299
Author(s):  
Ashish Shrestha ◽  
Bishal Ghimire ◽  
Francisco Gonzalez-Longatt
Keyword(s):  
Time Series ◽  
Kinetic Energy ◽  
Power System ◽  
Bayesian Model ◽  
Arima Model ◽  
Power Converter ◽  
Series Data ◽  
Percentage Error ◽  
Optimal Size ◽  
Short Term

Withthe massive penetration of electronic power converter (EPC)-based technologies, numerous issues are being noticed in the modern power system that may directly affect system dynamics and operational security. The estimation of system performance parameters is especially important for transmission system operators (TSOs) in order to operate a power system securely. This paper presents a Bayesian model to forecast short-term kinetic energy time series data for a power system, which can thus help TSOs to operate a respective power system securely. A Markov chain Monte Carlo (MCMC) method used as a No-U-Turn sampler and Stan’s limited-memory Broyden–Fletcher–Goldfarb–Shanno (LM-BFGS) algorithm is used as the optimization method here. The concept of decomposable time series modeling is adopted to analyze the seasonal characteristics of datasets, and numerous performance measurement matrices are used for model validation. Besides, an autoregressive integrated moving average (ARIMA) model is used to compare the results of the presented model. At last, the optimal size of the training dataset is identified, which is required to forecast the 30-min values of the kinetic energy with a low error. In this study, one-year univariate data (1-min resolution) for the integrated Nordic power system (INPS) are used to forecast the kinetic energy for sequences of 30 min (i.e., short-term sequences). Performance evaluation metrics such as the root-mean-square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and mean absolute scaled error (MASE) of the proposed model are calculated here to be 4.67, 3.865, 0.048, and 8.15, respectively. In addition, the performance matrices can be improved by up to 3.28, 2.67, 0.034, and 5.62, respectively, by increasing MCMC sampling. Similarly, 180.5 h of historic data is sufficient to forecast short-term results for the case study here with an accuracy of 1.54504 for the RMSE.

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