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The project aimed to implement Deep NN / RNN based solution in order to develop flexible methods that are able to adaptively fillin, backfill, and predict time-series using a large number of heterogeneous training datasets.

Over the past decade, bicycle-sharing systems have been growing in number and popularity in cities across the world. Bicycle-sharing systems allow users to rent bicycles on a very short-term basis for a price. This allows people to borrow a bike from point A and return it at point B, though they ... Read more

DSARF: Deep Switching Auto-Regressive Factorization: Application to Time Series Forecasting (AAAI2021)

Practical Deep Learning for Time Series / Sequential Data using fastai1/ Pytorch

Deep Probabilistic Koopman: long-term time-series forecasting under quasi-periodic uncertainty

Time Series Modeling: ML and Deep Learning Approaches with Python

Modeling and forecasting time series using deep learning

This project is essentially the implementation of the paper “Algorithmic Financial Trading with Deep Convolutional Neural Networks: Time Series to Image Conversion Approach”

A PyTorch implementation of Factorized Inference in Deep Markov Models for Incomplete Multimodal Time Series (https://arxiv.org/abs/1905.13570).

Official codes for "Addressing Information Asymmetry: Deep Temporal Causality Discovery for Mixed Time Series" (TPAMI 2025)