UNVEILING PATTERNS IN TIME SERIES FORECASTING MODELS - ARIMA, ARIMAX, VAR

Harpinder Kaur; Atendra Singh Yadav

UNVEILING PATTERNS IN TIME SERIES FORECASTING MODELS - ARIMA, ARIMAX, VAR

Authors: Harpinder Kaur, Atendra Singh Yadav

Unique Paper ID: 165449
Volume: 11
Issue: 1
PageNo: 1335-1345

Keywords: ARIMA ARIMAX Time series forecasting prediction VAR

Abstract:
Time series forecasting is an essential analytical method used in many different fields, including supply chain management, meteorology, finance, and economics. Its goal is to estimate future values by using patterns in previous data. This work offers a thorough introduction to time series forecasting techniques, clarifying key elements like trend, seasonality, cyclical patterns, and random variations. It highlights how crucial it is to handle missing values, smooth out data, and convert non-stationary data into stationary formats. Time series forecasting is a pivotal analytical tool that involves predicting future values based on previously observed data points collected over time. This research delves into the intricate methodologies and techniques utilized in time series forecasting, encompassing a wide array of models such as ARIMA, ARIMAX, and Vector AutoRegression. The paper also addresses the inherent challenges in time series forecasting, including data quality, stationarity requirements, model complexity, and the accurate modeling of seasonal and cyclical patterns. By leveraging robust time series models, organizations can enhance decision-making, optimize operations, and anticipate future trends. The research underscores the ongoing advancements and future directions in time series forecasting, emphasizing the integration of machine learning with traditional methods, real-time forecasting capabilities, and the incorporation of external variables to improve model accuracy and applicability.

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Cite This Article

ISSN: 2349-6002
Volume: 11
Issue: 1
PageNo: 1335-1345

UNVEILING PATTERNS IN TIME SERIES FORECASTING MODELS - ARIMA, ARIMAX, VAR

Available:https://ijirt.org/Article?manuscript=165449

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