International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 12 Issue: 05 | May 2025
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Can Deep Learning Predict the Indian Recession Movika Bhagavakar and Shivani Patel Master of Science in Computer Engineering Bhagwan Mahavir College of Engineering and Technology, Surat, India ---------------------------------------------------------------------------***-------------------------------------------------------------------------Abstract—The purpose of this research project is supply, and the global WTI crude oil price. These variables to create a prediction model that forecasts the likelihood of an Indian recession using the LSTM Model of Deep Learning. The Con- sumer Price Index (CPI), interest rates, bonds, the M3 money supply, and the global WTI oil price are some of the important economic indicators included in the study.By examining these factors, the study aims to determine how they affect India’s risk of experiencing an economic downturn. The findings will give investors and governments important information they can use to reduce possible risks and make well-informed decisions about economic stability.
Index Terms—Recession Prediction, LSTM Model, Economic Indicators, Indian Economy, Risk Mitigation, I. INTRODUCTION Economic stability is a cornerstone of sustainable development and growth, particularly in emerging economies like India. As one of the fastest-growing major economies in the world, India remains highly sensitive to both domestic and international economic fluctuations. Periods of recession not only affect the country’s GDP growth and employment levels, but also have far-reaching implications for public policy, investor confidence, and socio-economic well-being [1], [2]. Predicting recessions has traditionally relied on macroeconomic models and leading indicators such as inflation, interest rates, and fiscal balances. However, these approaches often lack the flexibility to capture nonlinear and complex interdependencies in the modern globalized economy. Recent advances in artificial intelligence, particularly deep learning models such as Long ShortTerm Memory (LSTM) networks, have demonstrated promising results in time-series forecasting and economic prediction tasks [3], [4]. This research aims to build a robust and interpretable prediction model to estimate the likelihood of an economic re- cession in India using LSTM-based deep learning techniques. The model incorporates a range of influential macroeconomic indicators, including the Consumer Price Index (CPI), interest rates, bond yields, the M3 money
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were selected based on their historical relevance to economic cycles and their capacity to reflect underlying economic health [5], [6].
By analyzing the dynamic relationships among these indicators over time, the study seeks to uncover patterns that precede economic downturns. The overarching objective is to equip policymakers, financial institutions, and investors with predictive insights that can help mitigate the risks associated with recessions. A reliable forecasting model can not only provide early warnings but also support the formulation of timely economic interventions to maintain stability. The remainder of this paper is organized as follows: Section II provides a comprehensive review of relevant literature on recession forecasting and the application of deep learning models in macroeconomic analysis. Section III outlines the methodological framework, including the LSTM architecture and data processing techniques. Section IV details the sources and characteristics of the macroeconomic indicators used in the study. Section V presents the results of the LSTM model and discusses its predictive capability. Section VI offers a statistical evaluation of variable relationships through correlation and regression testing. Section VII visualizes key economic indicators to support exploratory analysis and model interpretation. Finally, Section VIII concludes the paper with key insights, implications, and directions for future research.
II. LITERATURE REVIEW Forecasting economic downturns has long been a subject of interest among economists, policymakers, and data scientists. Traditional econometric models often fall short in identifying complex, nonlinear patterns across diverse macroeconomic in- dicators. In response, recent studies have increasingly applied machine learning and deep learning techniques to capture such relationships with improved predictive accuracy. ResearchGate studies have shown that machine learning methods such as Random Forests can be employed effectively to forecast the possibility of Indian recessions by analyzing various macroeconomic indicators [7]. Similarly,
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