Estimation of International Gold Price by Fusing Deep/Shallow Machine Learning
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A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
No Canadian affiliation. An affiliation-only frame — the usual design — would never have seen this work. It is one of the works that make the case for inverting the frame.
Post-publication record
- Nature
- Retraction
- Reason
- Concerns/Issues about Data;Concerns/Issues about Results and/or Conclusions;Concerns/Issues about Referencing/Attributions;Concerns/Issues about Peer Review;Investigation by Journal/Publisher;Investigation by Third Party;Paper Mill;Computer-Aided Content or Computer-Generated Content;Unreliable Results and/or Conclusions;
- Date
- 8/9/2023 0:00
- Flagged by OpenAlex?
- Yes
Source: Retraction Watch, joined by DOI. OpenAlex records retraction as is_retracted, a boolean over a state space with at least four values, so it cannot express an expression of concern, a correction or a reinstatement — it reports them as false, which reads as “fine”.
Abstract
In this work, we propose a new method that combines the support vector machine (SVM) and the long short-term memory (LSTM) model utilizing the theory of quotient space to predict the price of gold by leveraging the price factors that have supposedly an impact on the gold price. The Pearson correlation coefficient is employed to measure the relations between nine price factors and gold price. The five price factors with larger correlation coefficients are picked. Then, by utilizing the Granger causality test, the gold price may change concerning the two price factors when time is a concern, which results in combining the results of the correlation analysis with the results of Granger causality leading to a total of seven price factors. Also, the gold price can be divided into the quarters of the year according to the theory of the quotient space and temporal attribute. With three granularities per month, a 3-layer quotient space is constructed based on the synthesized and calculated granularities. The proposed method provides the prediction results that are compared with the predicted values of some grey models (GM) and the actual gold price, respectively. The results suggested that the prediction results of gold price have a comparable lower error measurement and perform better.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
The record
- Venue
- Journal of Advanced Transportation
- Topic
- Market Dynamics and Volatility
- Field
- Economics, Econometrics and Finance
- Canadian institutions
- —
- Funders
- —
- Keywords
- Gold as an investmentGranger causalitySupport vector machineQuotientGold standard (test)EconometricsCorrelation coefficientSpace (punctuation)Measure (data warehouse)Artificial intelligenceCorrelationRange (aeronautics)Price levelComputer scienceMathematicsMachine learningStatisticsEconomicsData miningFinancial economicsEngineering
- Has abstract in OpenAlex
- yes