A self-adapting multi-LSTM ensemble regression mode for failure prediction of transmission line network from wireless mesh nodes’ data

2020 ◽  
pp. 1-9
Author(s):  
Hongbin Sun ◽  
Mingjun Liu ◽  
Zhejun Qing ◽  
Chandler Miller
Keyword(s):  
Time Series ◽  
Transmission Line ◽  
Time Series Data ◽  
Failure Prediction ◽  
Series Data ◽  
Good Prediction ◽  
Similar Time ◽  
Prediction Ability ◽  
Wireless Mesh ◽  
Ensemble Regression

Transmission lines’ condition monitoring is an important part of smart grid construction. To ensure fast and efficient transmission of data, many mash-based wireless networks devices are adopted to collect status information. Since these nodes are exposed to the natural environment, vulnerable to damage, so it is very necessary to be predicting nodes’ fault. However, these mesh nodes are affected by a variety of complex and time-series factors, and traditional models are difficult to achieve effective failure prediction. To solve this problem, this paper proposes a self-adapting multi-LSTM ensemble regression model for transmission line network’s wireless mesh node failure prediction (MLSTM-FP), through establishes the corresponding relationship between similar time factors and LSTMs, the proposed model can realize multi time series data self-adapting and accurate failure prediction of transmission line network’s wireless mesh nodes, The experimental results show that the proposed method has a good prediction ability than traditional methods.

Short-term wind power forecasts by a synthetical similar time series data mining method

2018 ◽  
Vol 115 ◽  
pp. 575-584 ◽  
Author(s):  
Gaiping Sun ◽  
Chuanwen Jiang ◽  
Pan Cheng ◽  
Yangyang Liu ◽  
Xu Wang ◽  
...  
Keyword(s):  
Data Mining ◽  
Time Series ◽  
Wind Power ◽  
Time Series Data ◽  
Series Data ◽  
Mining Method ◽  
Short Term ◽  
Similar Time ◽  
Time Series Data Mining ◽  
Data Mining Method

Power Consumption Forecasting Scheme for Educational Institutions Based on Analysis of Similar Time Series Data

2017 ◽  
Vol 44 (9) ◽  
pp. 954-965 ◽  
Author(s):  
Jihoon Moon ◽  
Jinwoong Park ◽  
Sanghoon Han ◽  
Eenjun Hwang
Keyword(s):  
Time Series ◽  
Power Consumption ◽  
Time Series Data ◽  
Series Data ◽  
Educational Institutions ◽  
Similar Time

scholarly journals Likelihood-based estimation of continuous-time epidemic models from time-series data: application to measles transmission in London

2008 ◽  
Vol 5 (25) ◽  
pp. 885-897 ◽  
Author(s):  
Simon Cauchemez ◽  
Neil M Ferguson
Keyword(s):  
Time Series ◽  
Discrete Time ◽  
Continuous Time ◽  
Generation Time ◽  
Data Augmentation ◽  
Time Series Data ◽  
Observation Interval ◽  
Series Data ◽  
Time Model ◽  
Similar Time

We present a new statistical approach to analyse epidemic time-series data. A major difficulty for inference is that (i) the latent transmission process is partially observed and (ii) observed quantities are further aggregated temporally. We develop a data augmentation strategy to tackle these problems and introduce a diffusion process that mimicks the susceptible–infectious–removed (SIR) epidemic process, but that is more tractable analytically. While methods based on discrete-time models require epidemic and data collection processes to have similar time scales, our approach, based on a continuous-time model, is free of such constraint. Using simulated data, we found that all parameters of the SIR model, including the generation time, were estimated accurately if the observation interval was less than 2.5 times the generation time of the disease. Previous discrete-time TSIR models have been unable to estimate generation times, given that they assume the generation time is equal to the observation interval. However, we were unable to estimate the generation time of measles accurately from historical data. This indicates that simple models assuming homogenous mixing (even with age structure) of the type which are standard in mathematical epidemiology miss key features of epidemics in large populations.

scholarly journals Optimization Algorithm of Tourism Security Early Warning Information System Based on Long Short-Term Memory (LSTM)

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Lei Feng ◽  
Yukai Hao
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Information System ◽  
Optimization Algorithm ◽  
Early Warning ◽  
Time Series Data ◽  
Tourism Industry ◽  
Series Data ◽  
Prediction Ability ◽  
Analyze Data

Tourism safety is the focus of the tourism industry. It is not only related to the safety of tourists’ lives and property, but also related to social stability and sustainable development of the tourism industry. However, the security early warning of many scenic spots focuses on the response measures and remedial plans after the occurrence of security incidents, and the staff of many scenic spots have limited security awareness and information analysis ability, which is prone to lag in information release, and do not pay attention to the information of potential security problems. Therefore, this paper studies the optimization algorithm of the tourism security early warning information system based on the LSTM model and uses the recurrent neural network and LSTM to improve the processing and prediction ability of time-series data. The experimental results show that the number of three hidden layers in the tourism security early warning information system based on the LSTM model can reduce the training time of the model and improve the performance. Compared with the tourism safety early warning information system based on the BP neural network, it has better accuracy and stability, has better processing and prediction ability for time series data, and can monitor and analyze data scientifically in real-time and dynamically analyze data.

scholarly journals Mixing ARMA Models with EGARCH Models and Using it in Modeling and Analyzing the Time Series of Temperature

2021 ◽  
pp. 2307-2326
Author(s):  
Abduljabbar Ali Mudhir
Keyword(s):  
Time Series ◽  
Mixed Model ◽  
Time Series Data ◽  
Series Data ◽  
Data Series ◽  
Arma Models ◽  
Prediction Ability ◽  
Proposed Model ◽  
Egarch Model ◽  
Unconditional Variance

In this article our goal is mixing ARMA models with EGARCH models and composing a mixed model ARMA(R,M)-EGARCH(Q,P) with two steps, the first step includes modeling the data series by using EGARCH model alone interspersed with steps of detecting the heteroscedasticity effect and estimating  the model's parameters and check the adequacy of the model. Also we are predicting the conditional variance and verifying it's convergence to the unconditional variance value. The second step includes mixing ARMA with EGARCH and using the mixed (composite) model in modeling time series data and predict future values then asses the prediction ability of the proposed model by using prediction error criterions.

Dynamic Predictions from Time Series Data — An Artificial Neural Network Approach

1997 ◽  
Vol 08 (06) ◽  
pp. 1345-1360 ◽  
Author(s):  
D. R. Kulkarni ◽  
J. C. Parikh ◽  
A. S. Pandya
Keyword(s):  
Time Series ◽  
Dynamic Systems ◽  
Time Series Data ◽  
Hybrid Approach ◽  
Computer Experiments ◽  
Series Data ◽  
Optimal Size ◽  
Good Prediction ◽  
Ann Model ◽  
Artificial Neural

A hybrid approach, incorporating concepts of nonlinear dynamics in artificial neural networks (ANN), is proposed to model a time series generated by complex dynamic systems. We introduce well-known features used in the study of dynamic systems — time delay τ and embedding dimension d — for ANN modeling of time series. These features provide a theoretical basis for selecting the optimal size for the number of neurons in the input layer. The main outcome of the new approach for such problems is that to a large extent it defines the ANN architecture, models the time series and gives good prediction. As a consequence, we have an integrated and systematic data-driven scheme for modeling time series data. We illustrate our method by considering computer generated periodic and chaotic time series. The ANN model developed gave excellent quality of fit for the training and test sets as well as for iterative dynamic predictions for future values of the two time series. Further, computer experiments were conducted by introducing Gaussian noise of various degrees in the two time series, to simulate real world effects. We find that up to a limit introduction of noise leads to a smaller network with good generalizing capability.

Time Series Data Mining

2014 ◽  
Vol 1 (4) ◽  
pp. 51-68 ◽  
Author(s):  
Daniel Hebert ◽  
Billie Anderson ◽  
Alan Olinsky ◽  
J. Michael Hardin
Keyword(s):  
Data Mining ◽  
Time Series ◽  
Time Series Data ◽  
Grocery Store ◽  
Series Data ◽  
Time Interval ◽  
Real World Data ◽  
Similar Time ◽  
Data Set ◽  
Time Series Data Mining

Modern technologies have allowed for the amassment of data at a rate never encountered before. Organizations are now able to routinely collect and process massive volumes of data. A plethora of regularly collected information can be ordered using an appropriate time interval. The data would thus be developed into a time series. Time series data mining methodology identifies commonalities between sets of time-ordered data. Time series data mining detects similar time series using a technique known as dynamic time warping (DTW). This research provides a practical application of time series data mining. A real-world data set was provided to the authors by dunnhumby. A time series data mining analysis is performed using retail grocery store chain data and results are provided.

scholarly journals Multivariable Time Series Prediction for the Icing Process on Overhead Power Transmission Line

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Peng Li ◽  
Na Zhao ◽  
Donghua Zhou ◽  
Min Cao ◽  
Jingjie Li ◽  
...  
Keyword(s):  
Time Series ◽  
Transmission Line ◽  
Time Series Data ◽  
Power Transmission ◽  
Time Series Prediction ◽  
Series Data ◽  
Power Transmission Line ◽  
Alarm Systems ◽  
History Of ◽  
Overhead Power Transmission Line

The design of monitoring and predictive alarm systems is necessary for successful overhead power transmission line icing. Given the characteristics of complexity, nonlinearity, and fitfulness in the line icing process, a model based on a multivariable time series is presented here to predict the icing load of a transmission line. In this model, the time effects of micrometeorology parameters for the icing process have been analyzed. The phase-space reconstruction theory and machine learning method were then applied to establish the prediction model, which fully utilized the history of multivariable time series data in local monitoring systems to represent the mapping relationship between icing load and micrometeorology factors. Relevant to the characteristic of fitfulness in line icing, the simulations were carried out during the same icing process or different process to test the model’s prediction precision and robustness. According to the simulation results for the Tao-Luo-Xiong Transmission Line, this model demonstrates a good accuracy of prediction in different process, if the prediction length is less than two hours, and would be helpful for power grid departments when deciding to take action in advance to address potential icing disasters.

Feature-aware forecasting of large-scale time series data sets

2020 ◽  
Vol 62 (3-4) ◽  
pp. 157-168
Author(s):  
Claudio Hartmann ◽  
Lars Kegel ◽  
Wolfgang Lehner
Keyword(s):  
Time Series ◽  
Large Scale ◽  
Missing Values ◽  
Time Series Data ◽  
Series Data ◽  
The Internet ◽  
Data Sets ◽  
Similar Time ◽  
Forecast Time ◽  
The Internet Of Things

AbstractThe Internet of Things (IoT) sparks a revolution in time series forecasting. Traditional techniques forecast time series individually, which becomes unfeasible when the focus changes to thousands of time series exhibiting anomalies like noise and missing values. This work presents CSAR, a technique forecasting a set of time series with only one model, and a feature-aware partitioning applying CSAR on subsets of similar time series. These techniques provide accurate forecasts a hundred times faster than traditional techniques, preparing forecasting for the arising challenges of the IoT era.

scholarly journals Multiple Variables Time Series Adaptive Prediction Model Based on Grey Theory

2012 ◽  
Vol 6-7 ◽  
pp. 97-103
Author(s):  
Xiao Lei Li ◽  
Jie Zhong Ma ◽  
Yang Ming Guo
Keyword(s):  
Complex System ◽  
Time Series ◽  
Prediction Model ◽  
Time Series Data ◽  
Fault Prediction ◽  
Series Data ◽  
Good Prediction ◽  
Grey Prediction ◽  
Adaptive Prediction ◽  
Multiple Variables

Fault prediction is critical to ensure the safety and reliability of complex system. The reported fault prediction methods have achieved some success in practical applications. Generally, the information used in fault prediction is always mined from multi-variable time series and small simple data. Thus, based on grey prediction theory, an adaptive prediction model with multi-variable small simple time series data is proposed. In this method, after analyzing the disadvantages of model, we modify the initial values and background values of model, and then the interrelations and characteristics of the multiple variables time series are taken into account. The results of experiment with a certain complex system show that the model has good prediction precision, which will be useful in applications.

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