Comparative Analysis of Selected Time Series Forecasting Approaches for Indian Markets

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62156489%3A43110%2F24%3A43925841" target="_blank" >RIV/62156489:43110/24:43925841 - isvavai.cz</a>

Result on the web

<a href="https://doi.org/10.11118/978-80-7509-990-7-0167" target="_blank" >https://doi.org/10.11118/978-80-7509-990-7-0167</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.11118/978-80-7509-990-7-0167" target="_blank" >10.11118/978-80-7509-990-7-0167</a>

Result language

angličtina

Original language name

Comparative Analysis of Selected Time Series Forecasting Approaches for Indian Markets

Original language description

Financial market analysis and prediction have been topics of interest to traders and investors for decades. This study assesses the performance of selected time series prediction methods like deep learning algorithms (Long short-term memory model (LSTM)), traditional statistical models (Seasonal Auto Regressive Integrated Moving Approach with eXogenous regressors (SARIMAX)), and advanced ensemble learning algorithms (XGBoost and FB-Prophet) using real-world data from the Indian financial market. The stock prices of Reliance Company serve as a case study, enabling a thorough evaluation of predictive accuracy and errors of the models. A pre-processing approach has been proposed and implemented, integrating significant economic factors (Gold Price, USD to INR conversion, Consumer Price Index (CPI), Wholesale Price Index (WPI) and Indian 10-year yield bond) and evaluated with technical metrics (Mean squared error, Mean Absolute Error and R2 Score). The study investigates how the inclusion of these factors impacts prediction accuracy across the selected time series prediction methods. The comparative evaluation of models before and after the pre-processing method sheds light on the evolving predictive accuracy of LSTM, SARIMAX, FB-Prophet, and XGBoost. The study showed that the SARIMAX (extension of ARIMA with seasonality and exogenous factors) and XGBOOST performed relatively well with the proposed approach while LSTM and FB prophet (though advanced) did not perform as expected in Indian financial markets. This research contributes to advancing the understanding of time series forecasting in the financial market of India, offering practical insights for decision-makers and researchers.

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

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OECD FORD branch

10103 - Statistics and probability

Project

<a href="/en/project/EF16_017%2F0002334" target="_blank" >EF16_017/0002334: Research Infrastructure for Young Scientists</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Publication year

2024

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

26th International Conference Economic Competitiveness and Sustainability: Proceedings

ISBN

978-80-7509-990-7

ISSN

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e-ISSN

—

Number of pages

20

Pages from-to

167-186

Publisher name

Mendelova univerzita v Brně

Place of publication

Brno

Event location

Brno

Event date

Mar 21, 2024

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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