Accurately predicting stock market movements remains a formidable challenge due to the inherent volatility and complex interdependencies among stocks. Although multi-scale Graph Neural Networks (GNNs) hold potential for modeling these relationships, they frequently neglect two key points: the subtle intra-attribute patterns within each stock affecting inter-stock correlation, and the biased attention to coarse- and fine-grained features during multi-scale sampling. To overcome these challenges, we introduce MS-HGFN (Multi-Scale Hierarchical Graph Fusion Network). The model features a hierarchical GNN module that forms dynamic graphs by learning patterns from intra-attributes and features from inter-attributes over different time scales, thus comprehensively capturing spatio-temporal dependencies. Additionally, a top-down gating approach facilitates the integration of multi-scale spatio-temporal features, preserving critical coarse- and fine-grained features without too much interference. Experiments utilizing real-world datasets from U.S. and Chinese stock markets demonstrate that MS-HGFN outperforms both traditional and advanced models, yielding up to a 1.4% improvement in prediction accuracy and enhanced stability in return simulations. The code is available at https://anonymous.4open.science/r/MS-HGFN.

Under fake pink cherry blossom, guests sipped House of Suntory cocktails and picked at plates of chicken karaage, prawn gyoza and cauliflower tempura from a kaitenzushi-style conveyor belt … This was the London launch of Waitrose's new Japanese range. But without knowing it, and even if you live hundreds of miles away, your food choices may have had a hand in shaping the supermarket's 26-dish Japan Menyū range. That is because it was developed with input from Tastewise, an artificial intelligence (AI) platform that analyses menus, social media and online recipes to pinpoint food trends. While many businesses and individuals are concerned that AI is going to eat their lunch rather than set the menu, the technology is becoming more prevalent in the food industry, with its use doubling since 2017, according to McKinsey's 2022 Global Survey on AI. This is probably because it offers under-pressure retailers and food manufacturers an understanding of what fickle shoppers will want to buy in the future. It takes a year to perfect a new food project, but even so most of them miss the mark, and in recent times, companies have instead been forced to play catch-up with trends that have exploded on social media.

Stock trend analysis has been an influential time-series prediction topic due to its lucrative and inherently chaotic nature. Many models looking to accurately predict the trend of stocks have been based on Recurrent Neural Networks (RNNs). However, due to the limitations of RNNs, such as gradient vanish and long-term dependencies being lost as sequence length increases, in this paper we develop a Transformer based model that uses technical stock data and sentiment analysis to conduct accurate stock trend prediction over long time windows. This paper also introduces a novel dataset containing daily technical stock data and top news headline data spanning almost three years. Stock prediction based solely on technical data can suffer from lag caused by the inability of stock indicators to effectively factor in breaking market news. The use of sentiment analysis on top headlines can help account for unforeseen shifts in market conditions caused by news coverage. We measure the performance of our model against RNNs over sequence lengths spanning 5 business days to 30 business days to mimic different length trading strategies. This reveals an improvement in directional accuracy over RNNs as sequence length is increased, with the largest improvement being close to 18.63% at 30 business days.

Market financial forecasting is a trending area in deep learning. Deep learning models are capable of tackling the classic challenges in stock market data, such as its extremely complicated dynamics as well as long-term temporal correlation. To capture the temporal relationship among these time series, recurrent neural networks are employed. However, it is difficult for recurrent models to learn to keep track of long-term information. Convolutional Neural Networks have been utilized to better capture the dynamics and extract features for both short- and long-term forecasting. However, semantic segmentation and its well-designed fully convolutional networks have never been studied for time-series dense classification. We present a novel approach to predict long-term daily stock price change trends with fully 2D-convolutional encoder-decoders. We generate input frames with daily prices for a time-frame of T days. The aim is to predict future trends by pixel-wise classification of the current price frame. We propose a hierarchical CNN structure to encode multiple price frames to multiscale latent representation in parallel using Atrous Spatial Pyramid Pooling blocks and take that temporal coarse feature stacks into account in the decoding stages. Our hierarchical structure of CNNs makes it capable of capturing both long and short-term temporal relationships effectively. The effect of increasing the input time horizon via incrementing parallel encoders has been studied with interesting and substantial changes in the output segmentation masks. We achieve overall accuracy and AUC of %78.18 and 0.88 for joint trend prediction over the next 20 days, surpassing other semantic segmentation approaches. We compared our proposed model with several deep models specifically designed for technical analysis and found that for different output horizons, our proposed models outperformed other models.

In order to support stable web-based applications and services, anomalies on the IT performance status have to be detected timely. Moreover, the performance trend across the time series should be predicted. In this paper, we propose SeqVL (Sequential VAE-LSTM), a neural network model based on both VAE (Variational Auto-Encoder) and LSTM (Long Short-Term Memory). This work is the first attempt to integrate unsupervised anomaly detection and trend prediction under one framework. Moreover, this model performs considerably better on detection and prediction than VAE and LSTM work alone. On unsupervised anomaly detection, SeqVL achieves competitive experimental results compared with other state-of-the-art methods on public datasets. On trend prediction, SeqVL outperforms several classic time series prediction models in the experiments of the public dataset.