Integrating Navier-Stokes equation and neoteric iForest-BorutaShap-Facebook’s prophet framework for stock market prediction: An application in Indian context

Ghosh, Indranil; Chaudhuri, Tamal Datta

doi:10.1016/j.eswa.2022.118391

Cited by 19 publications

(5 citation statements)

References 69 publications

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“…The portioning has been made in a forward-looking sequential manner, i.e., the first 80% of data samples from April 1, 2020, are used to train the models, and the leftover 20% of data points are used to test the respective models sequentially. The aforesaid training process has been documented to be effective for complex time series forecasting in literature ( Ghosh et al, 2022 , Ghosh and Datta Chaudhuri, 2022 ). The calibration of both UMAP-LSTM and ISOMAP-GBR frameworks has been accomplished in the training segment and verified in the test segment using the four performance indicators.…”

Section: Resultsmentioning

confidence: 99%

Role of proliferation COVID-19 media chatter in predicting Indian stock market: Integrated framework of nonlinear feature transformation and advanced AI

Ghosh

Alfaro

Gámez

et al. 2023

Expert Systems with Applications

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Section: Resultsmentioning

confidence: 99%

Role of proliferation COVID-19 media chatter in predicting Indian stock market: Integrated framework of nonlinear feature transformation and advanced AI

Ghosh

Alfaro

Gámez

et al. 2023

Expert Systems with Applications

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“…In the context of BorutaSHAP, the SHAP metric is utilized to ascertain variable relevance [ 117 ], often resulting in improved overall accuracy compared to Boruta [ 118 ]. Prior research has indicated that BorutaSHAP serves as a reliable feature selection technique [ 119 – 122 ], making it suitable for application within this study.…”

Section: Methodsmentioning

confidence: 99%

Understanding the determinants of vaccine hesitancy in the United States: A comparison of social surveys and social media

Sasse,

Mahabir,

Gkountouna

et al. 2024

PLoS ONE

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The COVID-19 pandemic prompted governments worldwide to implement a range of containment measures, including mass gathering restrictions, social distancing, and school closures. Despite these efforts, vaccines continue to be the safest and most effective means of combating such viruses. Yet, vaccine hesitancy persists, posing a significant public health concern, particularly with the emergence of new COVID-19 variants. To effectively address this issue, timely data is crucial for understanding the various factors contributing to vaccine hesitancy. While previous research has largely relied on traditional surveys for this information, recent sources of data, such as social media, have gained attention. However, the potential of social media data as a reliable proxy for information on population hesitancy, especially when compared with survey data, remains underexplored. This paper aims to bridge this gap. Our approach uses social, demographic, and economic data to predict vaccine hesitancy levels in the ten most populous US metropolitan areas. We employ machine learning algorithms to compare a set of baseline models that contain only these variables with models that incorporate survey data and social media data separately. Our results show that XGBoost algorithm consistently outperforms Random Forest and Linear Regression, with marginal differences between Random Forest and XGBoost. This was especially the case with models that incorporate survey or social media data, thus highlighting the promise of the latter data as a complementary information source. Results also reveal variations in influential variables across the five hesitancy classes, such as age, ethnicity, occupation, and political inclination. Further, the application of models to different MSAs yields mixed results, emphasizing the uniqueness of communities and the need for complementary data approaches. In summary, this study underscores social media data’s potential for understanding vaccine hesitancy, emphasizes the importance of tailoring interventions to specific communities, and suggests the value of combining different data sources.

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“…The 'fbprophet' library has been used for simulating the model, which additionally provides an automatic change point detection facility too. Due to these advantageous points, Facebook's Prophet has recently seen remarkable success in modeling complex time series variables [ 17 , 26 ].…”

Section: Methodsmentioning

confidence: 99%

Can financial stress be anticipated and explained? Uncovering the hidden pattern using EEMD-LSTM, EEMD-prophet, and XAI methodologies

Ghosh

Pamučar

2022

Complex Intell. Syst.

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Global financial stress is a critical variable that reflects the ongoing state of several key macroeconomic indicators and financial markets. Predictive analytics of financial stress, nevertheless, has seen very little focus in literature as of now. Futuristic movements of stress in markets can be anticipated if the same can be predicted with a satisfactory level of precision. The current research resorts to two granular hybrid predictive frameworks to discover the inherent pattern of financial stress across several critical variables and geography. The predictive structure utilizes the Ensemble Empirical Mode Decomposition (EEMD) for granular time series decomposition. The Long Short-Term Memory Network (LSTM) and Facebook’s Prophet algorithms are invoked on top of the decomposed components to scrupulously investigate the predictability of final stress variables regulated by the Office of Financial Research (OFR). A rigorous feature screening using the Boruta methodology has been utilized too. The findings of predictive exercises reveal that financial stress across assets and continents can be predicted accurately in short and long-run horizons even at the time of steep financial distress during the COVID-19 pandemic. The frameworks appear to be statistically significant at the expense of model interpretation. To resolve the issue, dedicated Explainable Artificial Intelligence (XAI) methods have been used to interpret the same. The immediate past information of financial stress indicators largely explains patterns in the long run, while short-run fluctuations can be tracked by closely monitoring several technical indicators.

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Integrating Navier-Stokes equation and neoteric iForest-BorutaShap-Facebook’s prophet framework for stock market prediction: An application in Indian context

Cited by 19 publications

References 69 publications

Role of proliferation COVID-19 media chatter in predicting Indian stock market: Integrated framework of nonlinear feature transformation and advanced AI

Role of proliferation COVID-19 media chatter in predicting Indian stock market: Integrated framework of nonlinear feature transformation and advanced AI

Understanding the determinants of vaccine hesitancy in the United States: A comparison of social surveys and social media

Can financial stress be anticipated and explained? Uncovering the hidden pattern using EEMD-LSTM, EEMD-prophet, and XAI methodologies

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