Library for time-series-forecasting-as-a-service.

Overview

TIMEX

Tests with PyTest Coverage PyPI PyPI - Downloads

TIMEX (referred in code as timexseries) is a framework for time-series-forecasting-as-a-service.

Its main goal is to provide a simple and generic tool to build websites and, more in general, platforms, able to provide the forecasting of time-series in the "as-a-service" manner.

This means that users should interact with the service as less as possible.

An example of the capabilities of TIMEX can be found at covid-timex.it
That website is built using the Dash, on which the visualization part of TIMEX is built. A deep explanation is available in the dedicated repository.

Installation

The main two dependencies of TIMEX are Facebook Prophet and PyTorch. If you prefer, you can install them beforehand, maybe because you want to choose the CUDA/CPU version of Torch.

However, installation is as simple as running:

pip install timexseries

Get started

Please, refer to the Examples folder. You will find some Jupyter Notebook which illustrate the main characteristics of TIMEX. A Notebook explaining the covid-timex.it website is present, along with the source code of the site, here.

Documentation

The full documentation is available at here.

Contacts

If you have questions, suggestions or problems, feel free to open an Issue. You can contact us at:

This project provides the code and datasets for 'CapSal: Leveraging Captioning to Boost Semantics for Salient Object Detection', CVPR 2019.

Code-and-Dataset-for-CapSal This project provides the code and datasets for 'CapSal: Leveraging Captioning to Boost Semantics for Salient Object Detec

lu zhang 48 Aug 19, 2022
Adaptive Graph Convolution for Point Cloud Analysis

Adaptive Graph Convolution for Point Cloud Analysis This repository contains the implementation of AdaptConv for point cloud analysis. Adaptive Graph

64 Dec 21, 2022
code for our ECCV-2020 paper: Self-supervised Video Representation Learning by Pace Prediction

Video_Pace This repository contains the code for the following paper: Jiangliu Wang, Jianbo Jiao and Yunhui Liu, "Self-Supervised Video Representation

Jiangliu Wang 95 Dec 14, 2022
Sibur challange 2021 competition - 6 place

sibur challange 2021 Решение на 6 место: https://sibur.ai-community.com/competitions/5/tasks/13 Скор 1.4066/1.4159 public/private. Архитектура - однос

Ivan 5 Jan 11, 2022
用opencv的dnn模块做yolov5目标检测,包含C++和Python两个版本的程序

yolov5-dnn-cpp-py yolov5s,yolov5l,yolov5m,yolov5x的onnx文件在百度云盘下载, 链接:https://pan.baidu.com/s/1d67LUlOoPFQy0MV39gpJiw 提取码:bayj python版本的主程序是main_yolov5.

365 Jan 04, 2023
Probabilistic Programming and Statistical Inference in PyTorch

PtStat Probabilistic Programming and Statistical Inference in PyTorch. Introduction This project is being developed during my time at Cogent Labs. The

Stefano Peluchetti 109 Nov 26, 2022
Github for the conference paper GLOD-Gaussian Likelihood OOD detector

FOOD - Fast OOD Detector Pytorch implamentation of the confernce peper FOOD arxiv link. Abstract Deep neural networks (DNNs) perform well at classifyi

17 Jun 19, 2022
Negative Sample is Negative in Its Own Way: Tailoring Negative Sentences forImage-Text Retrieval

NSGDC Some codes in this repo are copied/modified from opensource implementations made available by UNITER, PyTorch, HuggingFace, OpenNMT, and Nvidia.

Zhihao Fan 2 Nov 07, 2022
Gym environments used in the paper: "Developmental Reinforcement Learning of Control Policy of a Quadcopter UAV with Thrust Vectoring Rotors"

gym_multirotor Gym to train reinforcement learning agents on UAV platforms Quadrotor Tiltrotor Requirements This package has been tested on Ubuntu 18.

Aditya M. Deshpande 19 Dec 29, 2022
Software that can generate photos from paintings, turn horses into zebras, perform style transfer, and more.

CycleGAN PyTorch | project page | paper Torch implementation for learning an image-to-image translation (i.e. pix2pix) without input-output pairs, for

Jun-Yan Zhu 11.5k Dec 30, 2022
The Official Implementation of the ICCV-2021 Paper: Semantically Coherent Out-of-Distribution Detection.

SCOOD-UDG (ICCV 2021) This repository is the official implementation of the paper: Semantically Coherent Out-of-Distribution Detection Jingkang Yang,

Jake YANG 62 Nov 21, 2022
Generative Modelling of BRDF Textures from Flash Images [SIGGRAPH Asia, 2021]

Neural Material Official code repository for the paper: Generative Modelling of BRDF Textures from Flash Images [SIGGRAPH Asia, 2021] Henzler, Deschai

Philipp Henzler 80 Dec 20, 2022
[ICCV 2021] Focal Frequency Loss for Image Reconstruction and Synthesis

Focal Frequency Loss - Official PyTorch Implementation This repository provides the official PyTorch implementation for the following paper: Focal Fre

Liming Jiang 460 Jan 04, 2023
The full training script for Enformer (Tensorflow Sonnet) on TPU clusters

Enformer TPU training script (wip) The full training script for Enformer (Tensorflow Sonnet) on TPU clusters, in an effort to migrate the model to pyt

Phil Wang 10 Oct 19, 2022
Python scripts form performing stereo depth estimation using the HITNET model in Tensorflow Lite.

TFLite-HITNET-Stereo-depth-estimation Python scripts form performing stereo depth estimation using the HITNET model in Tensorflow Lite. Stereo depth e

Ibai Gorordo 22 Oct 20, 2022
PyTorch-centric library for evaluating and enhancing the robustness of AI technologies

Responsible AI Toolbox A library that provides high-quality, PyTorch-centric tools for evaluating and enhancing both the robustness and the explainabi

24 Dec 22, 2022
MoViNets PyTorch implementation: Mobile Video Networks for Efficient Video Recognition;

MoViNet-pytorch Pytorch unofficial implementation of MoViNets: Mobile Video Networks for Efficient Video Recognition. Authors: Dan Kondratyuk, Liangzh

189 Dec 20, 2022
(CVPR 2022 - oral) Multi-View Depth Estimation by Fusing Single-View Depth Probability with Multi-View Geometry

Multi-View Depth Estimation by Fusing Single-View Depth Probability with Multi-View Geometry Official implementation of the paper Multi-View Depth Est

Bae, Gwangbin 138 Dec 28, 2022
Code and training data for our ECCV 2016 paper on Unsupervised Learning

Shuffle and Learn (Shuffle Tuple) Created by Ishan Misra Based on the ECCV 2016 Paper - "Shuffle and Learn: Unsupervised Learning using Temporal Order

Ishan Misra 44 Dec 08, 2021
TargetAllDomainObjects - A python wrapper to run a command on against all users/computers/DCs of a Windows Domain

TargetAllDomainObjects A python wrapper to run a command on against all users/co

Podalirius 19 Dec 13, 2022