We developed a web application demo, dynamically monitoring and visualizing the traffic condition in New York City, with a focus on Manhattan. We took advantage of two public API providers: Tomtom and Openweather, for traffic-related streaming data including real-time intersection speed, historical density, and weather details. We proposed a novel congestion model and updated the traffic heatmap every 15 minutes. We implemented our visualization by Google Maps APIs and similar to Google Map layout, we quantified the congestion into three degrees and plotted streets with colors. Our demo achieved similar performance with Google Maps, but with more reasonable and accurate results. Our work can also accept streaming data to achieve high-level concurrency.

This paper examines the impact of climate policy uncertainty (CPU) on green stock and crude oil returns using a causality-in-quantile test and a novel wavelet-based time-varying parametric quantile regression (WTPV-QR). We find that CPU is a Granger cause of both green stocks and crude oil returns across all quantiles. The effects are asymmetric and heterogeneous, particularly in extreme markets, and vary across time and frequency scales. Notably, the onset of the Russia-Ukraine war amplified these impacts. Moreover, CPU generally has a stronger effect on crude oil returns than on green stock returns under extreme market conditions. Finally, the short-term impacts of CPU exceed the long-term effects.

The financial market has long been an important domain for the application of machine learning (ML). Since the 1980s, researchers have leveraged ML techniques to uncover patterns and trends in financial data. Yet, stock market prediction remains inherently challenging due to its volatility and non-linearity. This paper presents a comprehensive multi-model forecasting framework integrating classical statistical models and modern deep learning methods. We employ three baseline models—Linear Regression (LR), Autoregressive Integrated Moving Average (ARIMA), and Long Short-Term Memory (LSTM)—as well as two extended architectures: an ARIMA-LSTM ensemble and a Transformer-based model. Our experiments span five representative U.S. stocks (AAPL, KO, NVDA, PFE, TSLA), reflecting diverse volatility profiles. A unified preprocessing pipeline—including sliding window segmentation, normalization, and volatility-aware sampling—is applied across all models to ensure fair evaluation. Results show that while ARIMA performs best on volatile assets, LSTM benefits significantly from input normalization, and ensemble approaches offer enhanced robustness. This work contributes an extensible, model-agnostic framework for financial time series prediction, providing actionable insights for both academic research and practical trading applications.