Deep Learning Models for Time Series Forecasting

Collection of materials on Deep Learning techniques and methods for building and training models for time series forecasting.

This is a work in progress and this repo will be updated on regular basis.

Selected Topics in Machine Learning

• Reinforcement Learning and Game Theory

• Offline Reinforcement and Self-Supervised Learning

• NLP Concepts

• Transformers

• Large Language Models

• Bayesian Networks

• Probabilistic Machine Learning and Bayesian Methods

• Statistical Learning, Kernel Methods, Kolmogorov-Arnold Networks

• Optimization, Classification, Regression

• Causal Inference, Root Cause Analysis and Model Checking

• Image Processing and Computer Vision: Techniques and Algorithms

• Deep Learning and Neural Networks

• Deep Learning for solving Image Processing problems and Generative Tasks

• Deep Learning Models for Time Series Forecasting problems

• Deep Learning Models for Dynamical System Analysis

• Vector Retrieval Techniques and Algorithms

• Retrieval-Augmented Generation Concepts, Architectures and Algorithms

• Hypothesis Testing, Estimation of Treatment Effects and Generalized Synthetic Control

• Graphs and Dynamic Programming

• Dynamical Systems, State-Space Models, Chaos, Ergodicity

• Information Theory, Statistical Mechanics, Diffusion Models

• Queueing Networks, Queueing Theory, Reversible Stochastic Processes

• Spectral Analysis, Optimization in Spectral Domain, Spectral Domain Modeling

• Computability, Automata, Logic Systems, Formal Grammars and Theory of Parsing

• Thought Forming, Consciousness, Intelligent Machines, Semantic Inference

Note

This repository uses git Large File Storage feature. In order to download locally the large files (> 1MB) which are maintained by git LFS you will need to install the Git extension for versioning large files: https://git-lfs.com/