A Methodological Review on Time Series Forecasting by using ARIMA

DOI

10.2991/978-94-6463-662-8_55How to use a DOI?

Keywords

Time Series Forecasting; ARIMA; Ensemble Learning; Random Forest; XGBoost; FBProphet; Modeltime; LSTM

Abstract

Time series forecasting using ARIMA (AutoRegressive Integrated Moving Average) is well-liked and tested machine learning technique for projecting upcoming values depending on historical data or previous observations. Although ARIMA is a strong and popular technique for time series forecasting, because of its capacity to identify and predict temporal patterns in data, ARIMA models have a broad variety of applications in numerous areas. Energy forecasting, healthcare, supply chain management marketing, customer behaviour analysis, and environmental sciences are a few industries that frequently use ARIMA. These are a handful of the various uses for ARIMA models. In reality, ARIMA is a flexible technique, time series analysis are tailored to a variety of domains and particular forecasting applications. Ultimately, it does have some significant limitations. These drawbacks, which include sensitivity to parameters, seasonality limits, the inability to capture non-linear correlations, and data requirements, make it challenging for companies to employ ARIMA for forecasting. In light of these limitations, my survey suggests using the R language. Modeltime package in conjunction with ensemble learning models (Random Forest, XGBoost) FB Prophet, LSTM for forecasting. When these three models are taken into account for forecasting, the best forecasting values will be determined by computing each model’s MSE (Mean Squared Error) value, which will be used for production further.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - B. Dilip Kumar Reddy
AU  - J. Swami Naik
AU  - S. Vinay Kumar
AU  - Suresh Kumar
AU  - G. Haritha
AU  - M. Raghavendra Reddy
PY  - 2025
DA  - 2025/03/17
TI  - A Methodological Review on Time Series Forecasting by using ARIMA
BT  - Proceedings of the International Conference on Advanced Materials, Manufacturing and Sustainable Development (ICAMMSD 2024)
PB  - Atlantis Press
SP  - 709
EP  - 719
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6463-662-8_55
DO  - 10.2991/978-94-6463-662-8_55
ID  - Reddy2025
ER  -