DTDR–ALSTM: Extracting dynamic time-delays to reconstruct multivariate data for improving attention-based LSTM industrial time series prediction models

2021 ◽  
Vol 211 ◽  
pp. 106508
Author(s):  
Jince Li ◽  
Bo Yang ◽  
Hongguang Li ◽  
Yongjian Wang ◽  
Chu Qi ◽  
...  
Keyword(s):  
Time Series ◽  
Time Delays ◽  
Prediction Models ◽  
Time Series Prediction ◽  
Multivariate Data ◽  
Dynamic Time

scholarly journals A Period-Aware Hybrid Model Applied for Forecasting AQI Time Series

2020 ◽  
Vol 12 (11) ◽  
pp. 4730 ◽  
Author(s):  
Ping Wang ◽  
Hongyinping Feng ◽  
Guisheng Zhang ◽  
Daizong Yu
Keyword(s):  
Time Series ◽  
Air Quality ◽  
Hybrid Model ◽  
Prediction Models ◽  
Time Series Prediction ◽  
Input Vector ◽  
Period Function ◽  
Data Set ◽  
Luenberger Observer ◽  
Extraction Algorithm

An accurate, reliable and stable air quality prediction system is conducive to the public health and management of atmospheric ecological environment; therefore, many models, individual or hybrid, have been implemented widely to deal with the prediction problem. However, many of these models do not take into consideration or extract improperly the period information in air quality index (AQI) time series, which impacts the models’ learning efficiency greatly. In this paper, a period extraction algorithm is proposed by using a Luenberger observer, and then a novel period-aware hybrid model combined the period extraction algorithm and tradition time series models is build to exploit the comprehensive forecasting capacity to the AQI time series with nonlinear and non-stationary noise. The hybrid model requires a multi-phase implementation. In the first step, the Luenberger observer is used to estimate the implied period function in the one-dimensional AQI series, and then the analyzed time series is mapped to the period space through the function to obtain the period information sub-series of the original series. In the second step, the period sub-series is combined with the original input vector as input vector components according to the time points to establish a new data set. Finally, the new data set containing period information is applied to train the traditional time series prediction models. Both theoretical proof and experimental results obtained on the AQI hour values of Beijing, Tianjin, Taiyuan and Shijiazhuang in North China prove that the hybrid model with period information presents stronger robustness and better forecasting accuracy than the traditional benchmark models.

Airline Delay Time Series Differentials

2014 ◽  
Vol 1 (2) ◽  
pp. 64-76
Author(s):  
Ronald Wesonga ◽  
Fabian Nabugoomu ◽  
Brian Masimbi
Keyword(s):  
Time Series ◽  
Prediction Models ◽  
Time Series Prediction ◽  
Model Development ◽  
Differential Analysis ◽  
Management Styles ◽  
Flight Delays ◽  
Daily Data ◽  
Passenger Travel ◽  
Time Series Modelling

Flight delays affect passenger travel satisfaction and increase airline costs. The authors explore airline differences with a focus on their delays based on autoregressive integrated moving averages. Aviation daily data were used in the analysis and model development. Time series modelling for six airlines was done to predict delays as a function of airport's timeliness performance. Findings show differences in the time series prediction models by airline. Differential analysis in the time series prediction models for airline delay suggests variations in airline efficiencies though at the same airport. The differences could be attributed to different management styles in the countries where the airlines originate. Thus, to improve airport timeliness performance, the study recommends airline disaggregated studies to explore the dynamics attributable to determinants of airline unique characteristics.

scholarly journals Implicit Multi-Feature Learning for Dynamic Time Series Prediction of the Impact of Institutions

IEEE Access ◽  
2017 ◽  
Vol 5 ◽  
pp. 16372-16382 ◽  
Author(s):  
Xiaomei Bai ◽  
Fuli Zhang ◽  
Jie Hou ◽  
Feng Xia ◽  
Amr Tolba ◽  
...  
Keyword(s):  
Time Series ◽  
Time Series Prediction ◽  
Feature Learning ◽  
Dynamic Time ◽  
The Impact

scholarly journals Bandwidth Utilization Prediction in LAN Network Using Time Series Modeling

2019 ◽  
pp. 78-89
Keyword(s):  
Time Series ◽  
Network Traffic ◽  
Network Performance ◽  
Prediction Models ◽  
Time Series Prediction ◽  
Bandwidth Utilization ◽  
Research Issues ◽  
Network Traffic Analysis ◽  
Network Bandwidth ◽  
And Performance

monitoring the behavior of computer networks is essential for problem identification and optimal management. Part of this behavior to be monitored is the utilization of the network bandwidth. Several techniques are used to model and forecast network traffic such as time series models, modern data mining techniques, soft computing approaches, and neural networks are used for network traffic analysis and prediction. Efficient bandwidth utilization and optimization are very interesting research issues in effective networks because bandwidth is one of the most required and expensive Internet components needed today. It is generally known that the higher the bandwidth available, the better the network performance, thus an essential aid for network design and bandwidth wastage control and a need for traffic models which can capture the characteristics is necessary. In this paper, a time series prediction models were proposed for LAN office network bandwidth utilization. The proposed prediction models are tested by using evaluation metrics used in time series such as MSE and performance evaluation plot. Testing results show that the proposed models can enhance the detection of bandwidth traffic and provide an efficient tool for bandwidth utilization.

scholarly journals Correlation Determination between COVID-19 and Weather Parameters Using Time Series Forecasting: A Case Study in Pakistan

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Humera Batool ◽  
Lixin Tian
Keyword(s):  
Time Series ◽  
Prediction Models ◽  
Mean Squared Error ◽  
Time Series Prediction ◽  
Detailed Examination ◽  
Percentage Error ◽  
Mortality Data ◽  
Squared Error ◽  
Temperature And Humidity ◽  
Weather Parameters

Infectious diseases like COVID-19 spread rapidly and have led to substantial economic loss worldwide, including in Pakistan. The effect of weather on COVID-19 spreading needs more detailed examination, as some studies have claimed to mitigate its spread. COVID-19 was declared a pandemic by WHO and has been reported in about 210 countries worldwide, including Asia, Europe, the USA, and North America. Person-to-person contact and international air travel between the nations were the leading causes behind the spreading of SARS-CoV-2 from its point of origin, besides the natural forces. However, further spread and infection within the community or country can be aided by natural elements, such as the weather. Therefore, the correlation between COVID-19 and temperature can be better elucidated in countries like Pakistan, where SARS-CoV-2 has affected at least 0.37 million people. This study collected Pakistan’s COVID-19 infection and mortality data for ten months (March–December 2020). Related weather parameters, temperature, and humidity were also obtained for the same course of time. The collected data were processed and used to compare the performance of various time series prediction models in terms of mean squared error (MSE), root-mean-squared error (RMSE), and mean absolute percentage error (MAPE). This paper, using the time series model, estimates the effect of humidity, temperature, and other weather parameters on COVID-19 transmission by obtaining the correlation among the total infected cases and the number of deaths and weather variables in a particular region. Results depict that weather parameters hold more influence in evaluating the sum number of cases and deaths than other factors like community, age, and the total population. Therefore, temperature and humidity are salient parameters for predicting COVID-19 affected instances. Moreover, it is concluded that the higher the temperature, the lesser the mortality due to COVID-19 infection.

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