Forecasting and Analysing Time Series Data Using Deep Learning

Snigdha Sen; V. T. Rajashekar; N. Dharshan

doi:10.1007/978-981-99-3932-9_25

Forecasting and Analysing Time Series Data Using Deep Learning

Snigdha Sen^*
, V. T. Rajashekar
, N. Dharshan

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

Rising demands in investment in cryptocurrencies are being discussed of late in recent times. The most established and well-known cryptocurrency is Bitcoin. An accurate prediction of the bitcoin price will always attract more investors. This paper aims to demonstrate the effectiveness and appropriateness of several deep learning models in time series forecasting. This experiment makes use of the CoinDesk Bitcoin Dataset. Our results demonstrate that the Gated Recurrent Unit (GRU) based model surpasses all other models in accurately predicting bitcoin prices. We experimented with different DL (Deep Learning) models, ranging from a simple model to a complicated model. Standard metrics, such as Mean Absolute Error and MSE, have been used to analyse each model. In order to make better decisions in the near future, this study will benefit the finance industry.

Original language	English
Title of host publication	Intelligent Systems - Proceedings of 3rd International Conference on Machine Learning, IoT and Big Data ICMIB 2023
Editors	Siba K. Udgata, Srinivas Sethi, Xiao-Zhi Gao
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	279-291
Number of pages	13
ISBN (Print)	9789819939312
DOIs	https://doi.org/10.1007/978-981-99-3932-9_25
Publication status	Published - 2024
Event	3rd International Conference on Machine Learning, Internet of Things and Big Data, ICMIB 2023 - Sarang, India Duration: 10-03-2023 → 12-03-2023

Publication series

Name	Lecture Notes in Networks and Systems
Volume	728 LNNS
ISSN (Print)	2367-3370
ISSN (Electronic)	2367-3389

Conference

Conference	3rd International Conference on Machine Learning, Internet of Things and Big Data, ICMIB 2023
Country/Territory	India
City	Sarang
Period	10-03-23 → 12-03-23

All Science Journal Classification (ASJC) codes

Control and Systems Engineering
Signal Processing
Computer Networks and Communications

Access to Document

10.1007/978-981-99-3932-9_25

Cite this

Sen, S., Rajashekar, V. T., & Dharshan, N. (2024). Forecasting and Analysing Time Series Data Using Deep Learning. In S. K. Udgata, S. Sethi, & X.-Z. Gao (Eds.), Intelligent Systems - Proceedings of 3rd International Conference on Machine Learning, IoT and Big Data ICMIB 2023 (pp. 279-291). (Lecture Notes in Networks and Systems; Vol. 728 LNNS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-981-99-3932-9_25

Sen, Snigdha ; Rajashekar, V. T. ; Dharshan, N. / Forecasting and Analysing Time Series Data Using Deep Learning. Intelligent Systems - Proceedings of 3rd International Conference on Machine Learning, IoT and Big Data ICMIB 2023. editor / Siba K. Udgata ; Srinivas Sethi ; Xiao-Zhi Gao. Springer Science and Business Media Deutschland GmbH, 2024. pp. 279-291 (Lecture Notes in Networks and Systems).

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title = "Forecasting and Analysing Time Series Data Using Deep Learning",

abstract = "Rising demands in investment in cryptocurrencies are being discussed of late in recent times. The most established and well-known cryptocurrency is Bitcoin. An accurate prediction of the bitcoin price will always attract more investors. This paper aims to demonstrate the effectiveness and appropriateness of several deep learning models in time series forecasting. This experiment makes use of the CoinDesk Bitcoin Dataset. Our results demonstrate that the Gated Recurrent Unit (GRU) based model surpasses all other models in accurately predicting bitcoin prices. We experimented with different DL (Deep Learning) models, ranging from a simple model to a complicated model. Standard metrics, such as Mean Absolute Error and MSE, have been used to analyse each model. In order to make better decisions in the near future, this study will benefit the finance industry.",

author = "Snigdha Sen and Rajashekar, \{V. T.\} and N. Dharshan",

note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.; 3rd International Conference on Machine Learning, Internet of Things and Big Data, ICMIB 2023 ; Conference date: 10-03-2023 Through 12-03-2023",

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language = "English",

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series = "Lecture Notes in Networks and Systems",

publisher = "Springer Science and Business Media Deutschland GmbH",

pages = "279--291",

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Sen, S, Rajashekar, VT & Dharshan, N 2024, Forecasting and Analysing Time Series Data Using Deep Learning. in SK Udgata, S Sethi & X-Z Gao (eds), Intelligent Systems - Proceedings of 3rd International Conference on Machine Learning, IoT and Big Data ICMIB 2023. Lecture Notes in Networks and Systems, vol. 728 LNNS, Springer Science and Business Media Deutschland GmbH, pp. 279-291, 3rd International Conference on Machine Learning, Internet of Things and Big Data, ICMIB 2023, Sarang, India, 10-03-23. https://doi.org/10.1007/978-981-99-3932-9_25

Forecasting and Analysing Time Series Data Using Deep Learning. / Sen, Snigdha; Rajashekar, V. T.; Dharshan, N.
Intelligent Systems - Proceedings of 3rd International Conference on Machine Learning, IoT and Big Data ICMIB 2023. ed. / Siba K. Udgata; Srinivas Sethi; Xiao-Zhi Gao. Springer Science and Business Media Deutschland GmbH, 2024. p. 279-291 (Lecture Notes in Networks and Systems; Vol. 728 LNNS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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T1 - Forecasting and Analysing Time Series Data Using Deep Learning

AU - Sen, Snigdha

AU - Rajashekar, V. T.

AU - Dharshan, N.

N1 - Publisher Copyright: © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

PY - 2024

Y1 - 2024

N2 - Rising demands in investment in cryptocurrencies are being discussed of late in recent times. The most established and well-known cryptocurrency is Bitcoin. An accurate prediction of the bitcoin price will always attract more investors. This paper aims to demonstrate the effectiveness and appropriateness of several deep learning models in time series forecasting. This experiment makes use of the CoinDesk Bitcoin Dataset. Our results demonstrate that the Gated Recurrent Unit (GRU) based model surpasses all other models in accurately predicting bitcoin prices. We experimented with different DL (Deep Learning) models, ranging from a simple model to a complicated model. Standard metrics, such as Mean Absolute Error and MSE, have been used to analyse each model. In order to make better decisions in the near future, this study will benefit the finance industry.

AB - Rising demands in investment in cryptocurrencies are being discussed of late in recent times. The most established and well-known cryptocurrency is Bitcoin. An accurate prediction of the bitcoin price will always attract more investors. This paper aims to demonstrate the effectiveness and appropriateness of several deep learning models in time series forecasting. This experiment makes use of the CoinDesk Bitcoin Dataset. Our results demonstrate that the Gated Recurrent Unit (GRU) based model surpasses all other models in accurately predicting bitcoin prices. We experimented with different DL (Deep Learning) models, ranging from a simple model to a complicated model. Standard metrics, such as Mean Absolute Error and MSE, have been used to analyse each model. In order to make better decisions in the near future, this study will benefit the finance industry.

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Sen S, Rajashekar VT, Dharshan N. Forecasting and Analysing Time Series Data Using Deep Learning. In Udgata SK, Sethi S, Gao XZ, editors, Intelligent Systems - Proceedings of 3rd International Conference on Machine Learning, IoT and Big Data ICMIB 2023. Springer Science and Business Media Deutschland GmbH. 2024. p. 279-291. (Lecture Notes in Networks and Systems). doi: 10.1007/978-981-99-3932-9_25

Forecasting and Analysing Time Series Data Using Deep Learning

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