391 Open source projects that are alternatives of or similar to forecastVeg

A ninja python package that unifies the Google Earth Engine ecosystem.

This Project gives you an overall idea for Forecasting Mutual Funds

Code and models for the manuscript "Predicting Poverty and Developmental Statistics from Satellite Images using Multi-task Deep Learning"

H2O is an Open Source, Distributed, Fast & Scalable Machine Learning Platform: Deep Learning, Gradient Boosting (GBM) & XGBoost, Random Forest, Generalized Linear Modeling (GLM with Elastic Net), K-Means, PCA, Generalized Additive Models (GAM), RuleFit, Support Vector Machine (SVM), Stacked Ensembles, Automatic Machine Learning (AutoML), etc.

NeurIPS’20 | Build powerful ensemble class-imbalanced learning models via meta-knowledge-powered resampler. | 设计元知识驱动的采样器解决类别不平衡问题

Software for verifying weather forecasts

In depth analysis and forecasting of product sales based on the items, stores, transaction and other dependent variables like holidays and oil prices.

A Python library for integrating model-based and judgmental forecasting

This Python function dm_test implements the Diebold-Mariano Test (1995) to statistically test forecast accuracy equivalence for 2 sets of predictions with modification suggested by Harvey et. al (1997).

A ready-to-use curated list of Spectral Indices for Remote Sensing applications.

Self-supervised Point Cloud Prediction Using 3D Spatio-temporal Convolutional Networks

foot是一个集足球数据采集器,简单分析的项目.AI足球球探为程序全自动处理,全程无人为参与干预足球分析足球预测程序.程序根据各大指数多维度数据,结合作者多年足球分析经验,精雕细琢,集天地之灵气,汲日月之精华,历时七七四十九天,经Bug九九八十一个,编码而成.有兴趣的朋友,可以关注一下公众号AI球探(微信号ai00268).

Class-imbalanced / Long-tailed ensemble learning in Python. Modular, flexible, and extensible. | 模块化、灵活、易扩展的类别不平衡/长尾机器学习库

Earth Observation Data Access Gateway

ARIMA, SARIMA, SARIMAX and AutoARIMA models for time series analysis and forecasting in the browser and Node.js

Time Series Ensemble Forecasting

NeuralProphet - A simple forecasting model based on Neural Networks in PyTorch

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68th palce solution in Kaggle Humpback Whale Identification.

Prediction or detection of various medical ailments

Building Decision Trees From Scratch In Python

Materials for GWU DNSC 6279 and DNSC 6290.

R package for automation of machine learning, forecasting, feature engineering, model evaluation, model interpretation, data generation, and recommenders.

Semi-automatic tool for manual segmentation of multi-spectral and geo-spatial imagery.

A minimal benchmark for scalability, speed and accuracy of commonly used open source implementations (R packages, Python scikit-learn, H2O, xgboost, Spark MLlib etc.) of the top machine learning algorithms for binary classification (random forests, gradient boosted trees, deep neural networks etc.).

Tutorials and training material for the H2O Machine Learning Platform

MATLAB code for Relevance Vector Machine using SB2_Release_200.

Conformalized Quantile Regression

GPT-2 Telegram Chat bot

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Neuron class provides LNU, QNU, RBF, MLP, MLP-ELM neurons

Satellite image time series in R

PyForecast is a statistical modeling tool used by Reclamation water managers and reservoir operators to train and build predictive models for seasonal inflows and streamflows. PyForecast allows users to make current water-year forecasts using models developed with the program.

LONG-TERM SERIES FORECASTING WITH QUERYSELECTOR – EFFICIENT MODEL OF SPARSEATTENTION

Deep Learning UDF for KSQL, the Streaming SQL Engine for Apache Kafka with Elasticsearch Sink Example

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Prediction of Premier league standings using Poisson distribution

Das Nowcasting erstellt eine Schätzung des Verlaufs der Anzahl von bereits erfolgten SARS-CoV-2-Erkrankungsfällen in Deutschland unter Berücksichtigung des Diagnose-, Melde- und Übermittlungsverzugs.

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Sparkling Water provides H2O functionality inside Spark cluster

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A curated list of gradient boosting research papers with implementations.

This is a project of build knowledge graph course. The project leverages historical stock price, and integrates social media listening from customers to predict market Trend On Dow Jones Industrial Average (DJIA).

This project contains examples which demonstrate how to deploy analytic models to mission-critical, scalable production environments leveraging Apache Kafka and its Streams API. Models are built with Python, H2O, TensorFlow, Keras, DeepLearning4 and other technologies.

Examples of techniques for training interpretable ML models, explaining ML models, and debugging ML models for accuracy, discrimination, and security.

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H2O.ai Machine Learning Interpretability Resources

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Forecast stock prices using machine learning approach. A time series analysis. Employ the Use of Predictive Modeling in Machine Learning to Forecast Stock Return. Approach Used by Hedge Funds to Select Tradeable Stocks

Show how to perform fast retraining with LightGBM in different business cases