A Study on Performance Analysis of Short Term Internet Traffic Forecasting Models

Abstract Long short-term memory (LSTM) models provide high predictive performance through their ability to recognize longer sequences of time series data. More recently, bidirectional deep learning models (BiLSTM) have extended the LSTM capabilities by training the input data twice in forward and backward directions. In this paper, BiLSTM short term traffic forecasting models have been developed and evaluated using data from a calibrated micro-simulation model for a congested freeway in Melbourne, Australia. The simulation model was extensively calibrated and validated to a high degree of accuracy using field data collected from 55 detectors on the freeway. The base year simulation model was then used to generate loop detector data including speed, flow and occupancy which were used to develop and compare a number of LSTM models for short-term traffic prediction up to 60 minutes into the future. The modelling results showed that BiLSTM outperformed other predictive models for multiple prediction horizons for base year conditions. The simulation model was then adapted for future year scenarios where the traffic demand was increased by 25-100 percent to reflect potential future increases in traffic demands. The results showed superior performance of BiLSTM for multiple prediction horizons for all traffic variables.

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Performance Analysis of Internet Traffic Forecasting Model

Korean Journal of Applied Statistics ◽

10.5351/kjas.2011.24.2.307 ◽

2011 ◽

Vol 24 (2) ◽

pp. 307-313

Author(s):

S. Kim ◽

M.H. Ha ◽

J.Y. Jung

Keyword(s):

Performance Analysis ◽

Internet Traffic ◽

Forecasting Model ◽

Traffic Forecasting

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Electricity Load and Internet Traffic Forecasting Using Vector Autoregressive Models

Mathematics ◽

10.3390/math9182347 ◽

2021 ◽

Vol 9 (18) ◽

pp. 2347

Author(s):

Yunsun Kim ◽

Sahm Kim

Keyword(s):

Prediction Accuracy ◽

Internet Traffic ◽

Load Prediction ◽

Short Term ◽

Vector Autoregressive ◽

Traffic Forecasting ◽

Forecasting Performance ◽

High Prediction ◽

Univariate Model ◽

Prediction Techniques

This study was conducted to investigate the applicability of measuring internet traffic as an input of short-term electricity demand forecasts. We believe our study makes a significant contribution to the literature, especially in short-term load prediction techniques, as we found that Internet traffic can be a useful variable in certain models and can increase prediction accuracy when compared to models in which it is not a variable. In addition, we found that the prediction error could be further reduced by applying a new multivariate model called VARX, which added exogenous variables to the univariate model called VAR. The VAR model showed excellent forecasting performance in the univariate model, rather than using the artificial neural network model, which had high prediction accuracy in the previous study.

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Development and evaluation of bidirectional LSTM freeway traffic forecasting models using simulation data

Scientific Reports ◽

10.1038/s41598-021-03282-z ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Rusul L. Abduljabbar ◽

Hussein Dia ◽

Pei-Wei Tsai

Keyword(s):

Simulation Model ◽

Time Series Data ◽

Short Term Memory ◽

Superior Performance ◽

Series Data ◽

Short Term ◽

Traffic Demand ◽

Traffic Forecasting ◽

Forecasting Models ◽

Multiple Prediction

AbstractLong short-term memory (LSTM) models provide high predictive performance through their ability to recognize longer sequences of time series data. More recently, bidirectional deep learning models (BiLSTM) have extended the LSTM capabilities by training the input data twice in forward and backward directions. In this paper, BiLSTM short term traffic forecasting models have been developed and evaluated using data from a calibrated micro-simulation model for a congested freeway in Melbourne, Australia. The simulation model was extensively calibrated and validated to a high degree of accuracy using field data collected from 55 detectors on the freeway. The base year simulation model was then used to generate loop detector data including speed, flow and occupancy which were used to develop and compare a number of LSTM models for short-term traffic prediction up to 60 min into the future. The modelling results showed that BiLSTM outperformed other predictive models for multiple prediction horizons for base year conditions. The simulation model was then adapted for future year scenarios where the traffic demand was increased by 25–100 percent to reflect potential future increases in traffic demands. The results showed superior performance of BiLSTM for multiple prediction horizons for all traffic variables.

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An Improved Model for Short-Term Traffic Forecasting Considering Weather Impacts

CICTP 2017 ◽

10.1061/9780784480915.080 ◽

2018 ◽

Author(s):

Xinchao Chen ◽

Si Qin ◽

Jian Zhang ◽

Huachun Tan ◽

Yunxia Xu ◽

...

Keyword(s):

Short Term ◽

Traffic Forecasting ◽

Improved Model ◽

Weather Impacts

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Financial Performance of ECIL, Hyderabad – An Analysis

Adhyayan A Journal of Management Sciences ◽

10.21567/adhyayan.v3i2.10184 ◽

2013 ◽

Vol 3 (2) ◽

Author(s):

N. Jyothi ◽

Dr. T. Satyanarayana Chary

Keyword(s):

Public Sector ◽

Performance Analysis ◽

Private Sector ◽

Financial Performance ◽

Business Environment ◽

Present Position ◽

Short Term ◽

Comprehensive Manner

Financial performance of individual organizations differ very significantly, however, the performance is distinguishable between public sector companies and private sector companies as their nature and size of investment and business environment is different . The ECIL is a very vast growing company which requires additional funds on a regular basis, whether internal or external. Particularly, the company needs both long term and short-term finances in view of its present position and enormous scope for improvement in the services provided. The present paper is a modest attempt to discuss the financial performance analysis of ECIL, Hyderabad in terms operating profits, capital employed ratios and turnover in a comprehensive manner over a period of 10 years.

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