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-daily-test

日々のライブラリの練習

alpa

Training and serving large-scale neural networks

arch

ARCH models in Python

autots

Automated Time Series Forecasting

aws-amp

basicts

A Standard and Fair Time Series Forecasting Benchmark and Toolkit.

d2l-en

Interactive deep learning book with multi-framework code, math, and discussions. Adopted at 400 universities from 60 countries including Stanford, MIT, Harvard, and Cambridge.

darts

A python library for user-friendly forecasting and anomaly detection on time series.

deep-learning-time-series

List of papers, code and experiments using deep learning for time series forecasting

doctr

docTR (Document Text Recognition) - a seamless, high-performing & accessible library for OCR-related tasks powered by Deep Learning.

etna

時系列分析ライブラリETNAの練習

etna-1

ETNA – Time-Series Library

etsformer-practice

最先端時系列データ分析モデルETSformerを使ってみる

featuretools

An open source python library for automated feature engineering

forecast_loto

ロトの予測

gendis

Contains an implementation (sklearn API) of the algorithm proposed in "GENDIS: GEnetic DIscovery of Shapelets" and code to reproduce all experiments.

gfpgan

GFPGAN aims at developing Practical Algorithms for Real-world Face Restoration.

gorilla

Gorilla: An API store for LLMs

gpt_index

GPT Index is a project consisting of a set of data structures designed to make it easier to use large external knowledge bases with LLMs.

hyperts

A Full-Pipeline Automated Time Series (AutoTS) Analysis Toolkit.

instruct-pix2pix

jax

Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more

kats

Kats, a kit to analyze time series data, a lightweight, easy-to-use, generalizable, and extendable framework to perform time series analysis, from understanding the key statistics and characteristics, detecting change points and anomalies, to forecasting future trends.

langchain

⚡ Building applications with LLMs through composability ⚡

last

Code for paper titled "Learning Latent Seasonal-Trend Representations for Time Series Forecasting" in NeurIPS 2022

ltsf-linear

This is the official implementation for AAAI-23 paper "Are Transformers Effective for Time Series Forecasting?"

mean-shifted-anomaly-detection

Mean-Shifted Contrastive Loss for Anomaly Detection (AAAI 2023)

merlion

Merlion: A Machine Learning Framework for Time Series Intelligence

nerf-slam

Real-Time Dense Monocular SLAM with Neural Radiance Fields. https://arxiv.org/abs/2210.13641 + Sigma-Fusion: Probabilistic Volumetric Fusion for Dense Monocular SLAM https://arxiv.org/abs/2210.01276

nerfstudio

A collaboration friendly studio for NeRFs