| dc.description.abstract | This study focuses on analyzing and evaluating
predictive techniques for asset price forecasting, centering
attention on gold, real estate, and automobile markets. The
paper explores numerous algorithms, techniques, methods
and models utilized in foreseeing the values of these assets.
A thorough appraisal is conducted that which present
various procedures for forecasting the value of assets.
This exploration compares the pros, cons, and performance
metrics of the anticipating models applied in each
discipline. Remarkable attention is granted to the
forecasting ability of Convolutional Neural Networks
(CNN), fuzzy rule-based systems, deep learning (DL)
techniques, ensemble regression models, and other
machine learning (ML) algorithms. Moreover, the tasks of
data analysis, preprocessing and feature selection methods
in boosting prediction accuracy is investigated.
The review paper calls attention to the implications along
with applications of error-free asset value forecasting,
together with knowledge-based decision making, risk
mitigation in addition to investment strategies. Moreover,
it examines the challenges and limitations along with future
directions in the domain, highlighting the demand for
robust, compliant and interpretable forecasting models.
By assessing and differentiating the approaches and
outcomes of asset value prediction across contrasting
fields, this review delivers important insights appropriate
to researchers, professionals and decision-makers
concerned in the dynamics and predictive potentials of
these platforms. | en_US |
