Comparative Analysis of Selected Time Series Forecasting Approaches for Indian Markets

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Comparative Analysis of Selected Time Series Forecasting Approaches for Indian Markets

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Comparative Analysis of Selected Time Series Forecasting Approaches for Indian Markets

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10103 - Statistics and probability

Projekt

<a href="/cs/project/EF16_017%2F0002334" target="_blank" >EF16_017/0002334: Výzkumná infrastruktura pro mladé vědce</a><br>

Návaznosti

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

Rok uplatnění

2024

Kód důvěrnosti údajů

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

Název statě ve sborníku

26th International Conference Economic Competitiveness and Sustainability: Proceedings

ISBN

978-80-7509-990-7

ISSN

—

e-ISSN

—

Počet stran výsledku

20

Strana od-do

167-186

Název nakladatele

Mendelova univerzita v Brně

Místo vydání

Brno

Místo konání akce

Brno

Datum konání akce

21. 3. 2024

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

—