#计算机科学#An open-source, low-code machine learning library in Python
#时序数据库#Automatic extraction of relevant features from time series:
#计算机科学#Time series Timeseries Deep Learning Machine Learning Python Pytorch fastai | State-of-the-art Deep Learning library for Time Series and Sequences in Pytorch / fastai
#大语言模型#TimeGPT-1: production ready pre-trained Time Series Foundation Model for forecasting and anomaly detection. Generative pretrained transformer for time series trained on over 100B data points. It's ca...
#计算机科学#List of papers, code and experiments using deep learning for time series forecasting
#自然语言处理#Jupyter Notebook tutorials on solving real-world problems with Machine Learning & Deep Learning using PyTorch. Topics: Face detection with Detectron 2, Time Series anomaly detection with LSTM Autoenco...
#时序数据库#Algorithms for outlier, adversarial and drift detection
#计算机科学#About Code release for "Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecasting" (NeurIPS 2021), https://arxiv.org/abs/2106.13008
#时序数据库#1st place solution
#自然语言处理#Awesome Artificial Intelligence, Machine Learning and Deep Learning as we learn it. Study notes and a curated list of awesome resources of such topics.
#计算机科学#Synthetic data generators for tabular and time-series data
#计算机科学#Time series forecasting with machine learning models
#时序数据库#Notebooks for financial economics. Keywords: Jupyter notebook pandas Federal Reserve FRED Ferbus GDP CPI PCE inflation unemployment wage income debt Case-Shiller housing asset portfolio equities SPX ...
#计算机科学#Unified Training of Universal Time Series Forecasting Transformers
#计算机科学#A framework for using LSTMs to detect anomalies in multivariate time series data. Includes spacecraft anomaly data and experiments from the Mars Science Laboratory and SMAP missions.
#自然语言处理#Lightweight, useful implementation of conformal prediction on real data.
#时序数据库#Power Tools for AI Engineers With Deadlines
#时序数据库#A python library for time-series smoothing and outlier detection in a vectorized way.
#时序数据库#Automatically build ARIMA, SARIMAX, VAR, FB Prophet and XGBoost Models on Time Series data sets with a Single Line of Code. Created by Ram Seshadri. Collaborators welcome.