A study of Autoregressive Model Using Time Series Analysis through Python

Title

A study of Autoregressive Model Using Time Series Analysis through Python

Creator

Kaushik J.; Vashisht A.V.

Description

A Time-series investigation is a simple technique for dividing information from reconsideration perceptions on a solitary unit or individual at ordinary stretches over countless perceptions. Timeseries examination can be considered to be the model of longitudinal plans. The most widely used method is focused on a class of Auto-Regressive Moving Average (ARMA) models. ARMA models could examine various examination questions, including fundamental cycle analysis, intercession analysis, and long-term therapy impact analysis. The model ID process, the meanings of essential concepts, and the factual assessment of boundaries are all depicted as specialized components of ARMA models. To explain the models, Multiunit time-series plans, multivariate time-series analysis, the consideration of variables, and the study of examples of intra-individual contrasts across time are all ongoing improvements to ARMA demonstrating techniques. [1] 2022 IEEE.

Source

Proceedings - 2022 4th International Conference on Advances in Computing, Communication Control and Networking, ICAC3N 2022, pp. 89-94.

Date

2022-01-01

Publisher

Institute of Electrical and Electronics Engineers Inc.

Subject

ARMA; Moving Average; Time series

Coverage

Kaushik J., Mit (ADT) University, Pune, India, Christ University, Pune, India; Vashisht A.V., Mit (ADT) University, Pune, India

Rights

Restricted Access

Relation

ISBN: 978-166547436-8

Format

Online

Language

English

Type

Conference paper

Identifier

https://doi.org/10.1109/ICAC3N56670.2022.10074077

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