Paper-Reproduce: (ESWA) Forecasting the realized volatility of stock price index: A hybrid model integrating CEEMDAN and LSTM
A forecasting model for the wholesale price index (WPI) of vegetables in India using CEEMDAN-VMD decomposition and LSTM to capture complex trends. The model offers accurate predictions to aid farmers, distributors, and policymakers in managing market stability, using data from 2013 to 2023.
Multivariate Gold Futures Price Prediction Based on ATT-CEEMDAN-CNN-BiLSTM Network
CEEMDAN-Informer-LSTM financial time series forecasting pipeline with Kedro and Gradio GUI
Tensorflow implementation of conditional Generative Adversarial Networks (cGAN) and conditional Deep Convolutional Adversarial Networks (cDCGAN) for MANIST dataset.
Code release for "PredRNN++: Towards A Resolution of the Deep-in-Time Dilemma in Spatiotemporal Predictive Learning" (ICML 2018)
Code release for "Memory In Memory: A Predictive Neural Network for Learning Higher-Order Non-Stationarity from Spatiotemporal Dynamics" (CVPR 2019)
Include mostly all basic data structure and process APIs in Meteva lib. Supporting the analysis/MOS/verification .etc on both OBS and NWF model