Junqin Lin
Biographic Data
| ID | 9569600 |
|---|---|
| NAME | Junqin Lin |
| GIVEN NAMES | Junqin |
| FAMILY NAME | Lin |
| SIGNATURE | LIN J |
| AFFILIATIONS | First Affiliated Hospital of Jinan University |
| VERIFIED | No |
| TOTAL WORKS | 2 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 2 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2022 |
| LATEST PUBLICATION YEAR | 2025 |
| H-INDEX | 0 |
Enhancing return forecasting using LSTM with agent-based synthetic data
Financial markets, as complex adaptive systems, are characterized by historical data limitations, inherent evolution and non-stationarity, which challenge the effectiveness of deep learning models such as Long Short-Term Memory (LSTM). We address these challenges by generating synthetic data using Agent-Based Modeling (ABM) to simulate complex market conditions through “what-if” scenarios. Our method comprises three steps: (i) pre-training the LS…
Alcohol and Health Outcomes
An umbrella review of meta-analyses was performed to summarize the evidence of associations between alcohol consumption and health outcomes and to assess its credibility. Meta-analyses of prospective cohort studies reporting the associations of alcohol consumption with health outcomes were identified. We recalculated the random-effects summary effect size and 95% confidence interval, heterogeneity, and small-study effect for each meta-analysis an…
No prominent works on this page.
Alcohol and Health Outcomes
An umbrella review of meta-analyses was performed to summarize the evidence of associations between alcohol consumption and health outcomes and to assess its credibility. Meta-analyses of prospective cohort studies reporting the associations of alcohol consumption with health outcomes were identified. We recalculated the random-effects summary effect size and 95% confidence interval, heterogeneity, and small-study effect for each meta-analysis an…
Enhancing return forecasting using LSTM with agent-based synthetic data
Financial markets, as complex adaptive systems, are characterized by historical data limitations, inherent evolution and non-stationarity, which challenge the effectiveness of deep learning models such as Long Short-Term Memory (LSTM). We address these challenges by generating synthetic data using Agent-Based Modeling (ABM) to simulate complex market conditions through “what-if” scenarios. Our method comprises three steps: (i) pre-training the LS…
Alcohol Consumption and Health Effects (1 work) · Artificial Intelligence (1 work) · Cohort study (1 work) · Complex Systems and Time Series Analysis (1 work) · Computer Science (1 work) · Energy Load and Power Forecasting (1 work) · Internal Medicine (1 work) · Liver Disease Diagnosis and Treatment (1 work) · Medicine (1 work) · Meta-analysis (1 work)