Web Traffic Time Series Forecasting using ARIMA and LSTM

Registration ID: IJNRD_222454

Published ID: IJNRD2405634

DOI: Click Here to Get

Hemant Salunke , Amol Kalhapure , Avishkar More , Shreyash Jagtap , Prof. Tanmayee Kute

Web traffic, ARMA,ARIMA, LSTM, Time series forecasting

Web traffic forecasting is critical for effective resource allocation, load balancing, and improving user experience in web services. This study evaluates the performance of three prominent time series forecasting methods—AutoRegressive Integrated Moving Average (ARIMA), AutoRegressive Moving Average (ARMA), and Long Short-Term Memory (LSTM) networks—on web traffic data. ARIMA and ARMA are traditional statistical models known for their robustness in handling linear patterns and seasonality. In contrast, LSTM, a type of recurrent neural network, excels at capturing long-term dependencies and nonlinear patterns in sequential data.We conduct a comprehensive empirical analysis using a real-world web traffic dataset. The models are assessed based on their predictive accuracy, computational efficiency, and ability to handle the inherent volatility and irregularities in web traffic data. The results indicate that while ARIMA and ARMA provide competitive performance for short-term forecasts with relatively lower computational costs, LSTM demonstrates superior accuracy in capturing complex, long-term dependencies, albeit at the expense of higher computational resources.This paper contributes to the field by offering a comparative analysis of traditional statistical models and advanced deep learning techniques for web traffic forecasting, providing insights into their applicability and limitations. The findings suggest that a hybrid approach, leveraging the strengths of both methodologies, could potentially yield enhanced forecasting performance.

Published Paper   E-Certificate

Hemant Salunke, Amol Kalhapure, Avishkar More, Shreyash Jagtap, & Prof. Tanmayee Kute (May-2024). Web Traffic Time Series Forecasting using ARIMA and LSTM. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 9(5), g289-g293. https://ijnrd.org/papers/IJNRD2405634.pdf

Volume 9 Issue 5, May-2024

Pages : g289-g293

Paper Reg. ID: IJNRD_222454

Published Paper Id: IJNRD2405634

Downloads: 000121989

Research Area: Information Technology 

Country: Pune, Maharashtra , India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2405634.pdf

Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2405634

Journal Name: INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT(IJNRD)

ISSN: 2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar | ESTD YEAR: 2016

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