Tootfinder

D-PAD: Deep-Shallow Multi-Frequency Patterns Disentangling for Time Series Forecasting
Xiaobing Yuan, Ling Chen
https://arxiv.org/abs/2403.17814 https://…

D-PAD: Deep-Shallow Multi-Frequency Patterns Disentangling for Time Series Forecasting
In time series forecasting, effectively disentangling intricate temporal patterns is crucial. While recent works endeavor to combine decomposition techniques with deep learning, multiple frequencies may still be mixed in the decomposed components, e.g., trend and seasonal. Furthermore, frequency domain analysis methods, e.g., Fourier and wavelet transforms, have limitations in resolution in the time domain and adaptability. In this paper, we propose D-PAD, a deep-shallow multi-frequency pattern…

A Study on Stock Forecasting Using Deep Learning and Statistical Models
Himanshu Gupta, Aditya Jaiswal
https://arxiv.org/abs/2402.06689 https://

A Study on Stock Forecasting Using Deep Learning and Statistical Models
Predicting a fast and accurate model for stock price forecasting is been a challenging task and this is an active area of research where it is yet to be found which is the best way to forecast the stock price. Machine learning, deep learning and statistical analysis techniques are used here to get the accurate result so the investors can see the future trend and maximize the return of investment in stock trading. This paper will review many deep learning algorithms for stock price forecasting. …