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2018 | OriginalPaper | Buchkapitel

Improving Time Series Forecasting Using Mathematical and Deep Learning Models

verfasst von : Mohit Gupta, Ayushi Asthana, Nishant Joshi, Pulkit Mehndiratta

Erschienen in: Big Data Analytics

Verlag: Springer International Publishing

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Abstract

With the increase in the number of internet users, there is a deluge of traffic over the web, handling Internet traffic with much more optimized and efficient approach is the need of the hour. In this work, we have tried to forecast Internet traffic on TCP/IP network using web traffic data of Wikipedia articles, provided by Kaggle (https://www.kaggle.com/). We work on the stationarity of time series and use mathematical concepts of log transformation, differencing and decomposition in order to make the time series stationary. Our research presents an approach for forecasting web traffic for these articles using different statistical time series models such as Auto-Regressive (AR) model, Moving Average (MA) model, Auto-Regressive Integrated Moving Average (ARIMA) Model and a deep learning model - Long Short-Term Memory (LSTM). This research work opens the possibility of efficient traffic handling thus, leading to improved performance for an organization as well as better experience for the users on the internet

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Vorheriges Kapitel Homogenous Ensemble of Time-Series Models for Indian Stock Market

Nächstes Kapitel Emerging Technologies and Opportunities for Innovation in Financial Data Analytics: A Perspective

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Titel: Improving Time Series Forecasting Using Mathematical and Deep Learning Models
verfasst von: Mohit Gupta
Ayushi Asthana
Nishant Joshi
Pulkit Mehndiratta
Verlag: Springer International Publishing
Buch: Big Data Analytics
Print ISBN: 978-3-030-04779-5

Electronic ISBN: 978-3-030-04780-1

Copyright-Jahr: 2018
DOI: https://doi.org/10.1007/978-3-030-04780-1_8

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