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This repository contains a reading list of papers on Time Series Forecasting/Prediction (TSF) and Spatio-Temporal Forecasting/Prediction (STF). These papers are mainly categorized according to the …
美国华人技术创业的快速公司/银行/税务/投资操作白皮书
这是一个关于软件测试面试题目的列表，收集了关于测试理论，自动化测试，性能测试以及其他一些软件测试相关的面试题目。
小而美的自动化测试解决方案，支持 Web UI 测试，Http 接口测试，DB 操作测试，App 测试，小程序测试，Windows GUI 测试，文件操作
MeterSphere 是新一代的开源持续测试工具，让软件测试工作更简单、更高效，不再成为持续交付的瓶颈。
Cross-platform automation framework for all kinds of apps, built on top of the W3C WebDriver protocol
HttpRunner 是一个开源的 API/UI 测试工具，简单易用，功能强大，具有丰富的插件化机制和高度的可扩展能力。
# **ABSTRACT** Main Objective: The main agenda of this project is: Perform extensive Exploratory Data Analysis(EDA) on the Zomato Dataset. Build an appropriate Machine Learning Model that will hel…
KDD'22 ''Talent Demand-Supply Joint Prediction with Dynamic Heterogeneous Graph Enhanced Meta-Learning''
2021 - Github companion to "Demand Prediction in Retail: A Practical Guide to Leverage Data and Predictive Analytics" (Springer Series in Supply Chain Management, 14)
This project involves in predicting the demand for a product with new features based on the historical data of demand available for similar and currently existing products using Machine learning al…
Building Time series forecasting models, including the XGboost Regressor, GRU (Gated Recurrent Unit), LSTM (Long Short-Term Memory), CNN (Convolutional Neural Network), CNN-LSTM, and LSTM-Attention…
Demand forecasting of items using three step machine learning model. Clustering - Classification - Prediction
Open solution to the Avito Demand Prediction Challenge
Model to accurately forecast inventory demand based on historical sales data.
Machine Learning Model for Order Demand Prediction based on historical Order data - Built for Swiggy Hackathon 2018
You are opening a new Store at a particular location. Now, Given the Store Location, Area, Size and other params. Predict the overall revenue/Sale generation of the Store.
Sales forecasting for the supply chain industry.
Using this model, BigMart will try to understand the properties of products and stores which play a key role in increasing sales.
In depth analysis and forecasting of product sales based on the items, stores, transaction and other dependent variables like holidays and oil prices.
GluonTS Implementation of Intermittent Demand Forecasting with Deep Renewal Processes arXiv:1911.10416v1 [cs.LG]
Official implementation of Temporal-Guided Networks
Predicting sales of items in stores using Feed Forward Neural Network, Long Short Term Memory, Temporal Convolution Network & a hybrid of TCN and LSTM models.
Multivariate Time Series Prediction using Keras (CNN BiLSTM Attention)
[AAAI'19] Spatiotemporal Multi-Graph Convolution Network for Ride-Hailing Demand Forecasting (Pytorch Replication)
