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awesome-h2o
A curated list of research, applications and projects built using the H2O Machine Learning platform
https://github.com/h2oai/awesome-h2o
Last synced: 5 days ago
JSON representation
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Blog Posts & Tutorials
- Artificial Intelligence Made Easy with H2O.ai: A Comprehensive Guide to Modeling with H2O.ai and AutoML in Python
- Artificial Intelligence Made Easy with H2O.ai: A Comprehensive Guide to Modeling with H2O.ai and AutoML in Python
- Using H2O AutoML to simplify training process (and also predict wine quality)
- Visualizing ML Models with LIME
- Parallel Grid Search in H2O
- Importing, Inspecting and Scoring with MOJO models inside H2O
- Artificial Intelligence Made Easy with H2O.ai: A Comprehensive Guide to Modeling with H2O.ai and AutoML in Python
- Anomaly Detection With Isolation Forests Using H2O
- Predicting residential property prices in Bratislava using recipes - H2O Machine learning
- Inspecting Decision Trees in H2O
- Gentle Introduction to AutoML from H2O.ai
- Machine Learning With H2O — Hands-On Guide for Data Scientists
- Using machine learning with LIME to understand employee churn
- Analytics at Scale: h2o, Apache Spark and R on AWS EMR
- Automated and unmysterious machine learning in cancer detection
- Time series machine learning with h2o+timetk
- Sales Analytics: How to use machine learning to predict and optimize product backorders
- HR Analytics: Using machine learning to predict employee turnover
- Building deep neural nets with h2o and rsparkling that predict arrhythmia of the heart
- Predicting food preferences with sparklyr (machine learning)
- Moving largish data from R to H2O - spam detection with Enron emails
- Deep learning & parameter tuning with mxnet, h2o package in R
- Artificial Intelligence Made Easy with H2O.ai: A Comprehensive Guide to Modeling with H2O.ai and AutoML in Python
- Artificial Intelligence Made Easy with H2O.ai: A Comprehensive Guide to Modeling with H2O.ai and AutoML in Python
- Artificial Intelligence Made Easy with H2O.ai: A Comprehensive Guide to Modeling with H2O.ai and AutoML in Python
- Artificial Intelligence Made Easy with H2O.ai: A Comprehensive Guide to Modeling with H2O.ai and AutoML in Python
- Artificial Intelligence Made Easy with H2O.ai: A Comprehensive Guide to Modeling with H2O.ai and AutoML in Python
- Artificial Intelligence Made Easy with H2O.ai: A Comprehensive Guide to Modeling with H2O.ai and AutoML in Python
- Artificial Intelligence Made Easy with H2O.ai: A Comprehensive Guide to Modeling with H2O.ai and AutoML in Python
- Artificial Intelligence Made Easy with H2O.ai: A Comprehensive Guide to Modeling with H2O.ai and AutoML in Python
- Artificial Intelligence Made Easy with H2O.ai: A Comprehensive Guide to Modeling with H2O.ai and AutoML in Python
- Artificial Intelligence Made Easy with H2O.ai: A Comprehensive Guide to Modeling with H2O.ai and AutoML in Python
- Artificial Intelligence Made Easy with H2O.ai: A Comprehensive Guide to Modeling with H2O.ai and AutoML in Python
- Artificial Intelligence Made Easy with H2O.ai: A Comprehensive Guide to Modeling with H2O.ai and AutoML in Python
- Artificial Intelligence Made Easy with H2O.ai: A Comprehensive Guide to Modeling with H2O.ai and AutoML in Python
- Moving largish data from R to H2O - spam detection with Enron emails
- Autoencoders and anomaly detection with machine learning in fraud analytics
- Artificial Intelligence Made Easy with H2O.ai: A Comprehensive Guide to Modeling with H2O.ai and AutoML in Python
- Deep learning & parameter tuning with mxnet, h2o package in R
- Artificial Intelligence Made Easy with H2O.ai: A Comprehensive Guide to Modeling with H2O.ai and AutoML in Python
- Artificial Intelligence Made Easy with H2O.ai: A Comprehensive Guide to Modeling with H2O.ai and AutoML in Python
- Artificial Intelligence Made Easy with H2O.ai: A Comprehensive Guide to Modeling with H2O.ai and AutoML in Python
- Artificial Intelligence Made Easy with H2O.ai: A Comprehensive Guide to Modeling with H2O.ai and AutoML in Python
- Artificial Intelligence Made Easy with H2O.ai: A Comprehensive Guide to Modeling with H2O.ai and AutoML in Python
- Artificial Intelligence Made Easy with H2O.ai: A Comprehensive Guide to Modeling with H2O.ai and AutoML in Python
- Deep learning & parameter tuning with mxnet, h2o package in R
- Artificial Intelligence Made Easy with H2O.ai: A Comprehensive Guide to Modeling with H2O.ai and AutoML in Python
- Artificial Intelligence Made Easy with H2O.ai: A Comprehensive Guide to Modeling with H2O.ai and AutoML in Python
- Artificial Intelligence Made Easy with H2O.ai: A Comprehensive Guide to Modeling with H2O.ai and AutoML in Python
- Artificial Intelligence Made Easy with H2O.ai: A Comprehensive Guide to Modeling with H2O.ai and AutoML in Python
- Artificial Intelligence Made Easy with H2O.ai: A Comprehensive Guide to Modeling with H2O.ai and AutoML in Python
- Artificial Intelligence Made Easy with H2O.ai: A Comprehensive Guide to Modeling with H2O.ai and AutoML in Python
- Artificial Intelligence Made Easy with H2O.ai: A Comprehensive Guide to Modeling with H2O.ai and AutoML in Python
- Artificial Intelligence Made Easy with H2O.ai: A Comprehensive Guide to Modeling with H2O.ai and AutoML in Python
- Artificial Intelligence Made Easy with H2O.ai: A Comprehensive Guide to Modeling with H2O.ai and AutoML in Python
- Artificial Intelligence Made Easy with H2O.ai: A Comprehensive Guide to Modeling with H2O.ai and AutoML in Python
- Artificial Intelligence Made Easy with H2O.ai: A Comprehensive Guide to Modeling with H2O.ai and AutoML in Python
- Artificial Intelligence Made Easy with H2O.ai: A Comprehensive Guide to Modeling with H2O.ai and AutoML in Python
- Artificial Intelligence Made Easy with H2O.ai: A Comprehensive Guide to Modeling with H2O.ai and AutoML in Python
- Artificial Intelligence Made Easy with H2O.ai: A Comprehensive Guide to Modeling with H2O.ai and AutoML in Python
- Artificial Intelligence Made Easy with H2O.ai: A Comprehensive Guide to Modeling with H2O.ai and AutoML in Python
- Artificial Intelligence Made Easy with H2O.ai: A Comprehensive Guide to Modeling with H2O.ai and AutoML in Python
- Artificial Intelligence Made Easy with H2O.ai: A Comprehensive Guide to Modeling with H2O.ai and AutoML in Python
- Artificial Intelligence Made Easy with H2O.ai: A Comprehensive Guide to Modeling with H2O.ai and AutoML in Python
- Artificial Intelligence Made Easy with H2O.ai: A Comprehensive Guide to Modeling with H2O.ai and AutoML in Python
- Artificial Intelligence Made Easy with H2O.ai: A Comprehensive Guide to Modeling with H2O.ai and AutoML in Python
- Artificial Intelligence Made Easy with H2O.ai: A Comprehensive Guide to Modeling with H2O.ai and AutoML in Python
- Using H2O AutoML to simplify training process (and also predict wine quality)
- Artificial Intelligence Made Easy with H2O.ai: A Comprehensive Guide to Modeling with H2O.ai and AutoML in Python
- Artificial Intelligence Made Easy with H2O.ai: A Comprehensive Guide to Modeling with H2O.ai and AutoML in Python
- Artificial Intelligence Made Easy with H2O.ai: A Comprehensive Guide to Modeling with H2O.ai and AutoML in Python
- Artificial Intelligence Made Easy with H2O.ai: A Comprehensive Guide to Modeling with H2O.ai and AutoML in Python
- Artificial Intelligence Made Easy with H2O.ai: A Comprehensive Guide to Modeling with H2O.ai and AutoML in Python
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Research Papers
- Single-cell mass cytometry on peripheral blood identifies immune cell subsets associated with primary biliary cholangitis
- Algorithmic trading using deep neural networks on high frequency data
- Single-cell mass cytometry on peripheral blood identifies immune cell subsets associated with primary biliary cholangitis
- Algorithmic trading using deep neural networks on high frequency data
- Automated machine learning: AI-driven decision making in business analytics
- Water-Quality Prediction Based on H2O AutoML and Explainable AI Techniques
- Which model to choose? Performance comparison of statistical and machine learning models in predicting PM2.5 from high-resolution satellite aerosol optical depth
- Predicting Risk of Delays in Postal Deliveries with Neural Networks and Gradient Boosting Machines
- Maturity of gray matter structures and white matter connectomes, and their relationship with psychiatric symptoms in youth
- Appendectomy during the COVID-19 pandemic in Italy: a multicenter ambispective cohort study by the Italian Society of Endoscopic Surgery and new technologies (the CRAC study)
- Forecasting Canadian GDP Growth with Machine Learning
- Morphological traits of reef corals predict extinction risk but not conservation status
- Machine Learning as a Tool for Improved Housing Price Prediction
- Citizen Science Data Show Temperature-Driven Declines in Riverine Sentinel Invertebrates
- Stock Market Analysis using Stacked Ensemble Learning Method
- H2O AutoML: Scalable Automatic Machine Learning
- Single-cell mass cytometry on peripheral blood identifies immune cell subsets associated with primary biliary cholangitis
- Prediction of the functional impact of missense variants in BRCA1 and BRCA2 with BRCA-ML
- Innovative deep learning artificial intelligence applications for predicting relationships between individual tree height and diameter at breast height
- An Open Source AutoML Benchmark
- Machine Learning in Python: Main developments and technology trends in data science, machine learning, and artificial intelligence
- Human actions recognition in video scenes from multiple camera viewpoints - Moral-Hernandeza. (2019)
- Extending MLP ANN hyper-parameters Optimization by using Genetic Algorithm - Moral-Hernandez. (2018)
- askMUSIC: Leveraging a Clinical Registry to Develop a New Machine Learning Model to Inform Patients of Prostate Cancer Treatments Chosen by Similar Men
- Machine Learning Methods to Perform Pricing Optimization. A Comparison with Standard GLMs
- Comparative Performance Analysis of Neural Networks Architectures on H2O Platform for Various Activation Functions
- Algorithmic trading using deep neural networks on high frequency data
- Generic online animal activity recognition on collar tags
- Soil nutrient maps of Sub-Saharan Africa: assessment of soil nutrient content at 250 m spatial resolution using machine learning
- Robust and flexible estimation of data-dependent stochastic mediation effects: a proposed method and example in a randomized trial setting
- Automated versus do-it-yourself methods for causal inference: Lessons learned from a data analysis competition
- Using deep learning to predict the mortality of leukemia patients
- Use of a machine learning framework to predict substance use disorder treatment success
- Ultra-wideband antenna-induced error prediction using deep learning on channel response data
- Inferring passenger types from commuter eigentravel matrices
- Deep neural networks, gradient-boosted trees, random forests: Statistical arbitrage on the S&P 500
- Identifying IT purchases anomalies in the Brazilian government procurement system using deep learning
- Deep learning and association rule mining for predicting drug response in cancer
- The value of points of interest information in predicting cost-effective charging infrastructure locations
- Adaptive modelling of spatial diversification of soil classification units. Journal of Water and Land Development
- Scalable ensemble learning and computationally efficient variance estimation
- Superchords: decoding EEG signals in the millisecond range
- Single-cell mass cytometry on peripheral blood identifies immune cell subsets associated with primary biliary cholangitis
- Algorithmic trading using deep neural networks on high frequency data
- Single-cell mass cytometry on peripheral blood identifies immune cell subsets associated with primary biliary cholangitis
- Algorithmic trading using deep neural networks on high frequency data
- Predicting recovery of credit operations on a Brazilian bank
- Deep learning anomaly detection as support fraud investigation in Brazilian exports and anti-money laundering
- Single-cell mass cytometry on peripheral blood identifies immune cell subsets associated with primary biliary cholangitis
- Algorithmic trading using deep neural networks on high frequency data
- Single-cell mass cytometry on peripheral blood identifies immune cell subsets associated with primary biliary cholangitis
- Algorithmic trading using deep neural networks on high frequency data
- Single-cell mass cytometry on peripheral blood identifies immune cell subsets associated with primary biliary cholangitis
- Algorithmic trading using deep neural networks on high frequency data
- Single-cell mass cytometry on peripheral blood identifies immune cell subsets associated with primary biliary cholangitis
- Algorithmic trading using deep neural networks on high frequency data
- Algorithmic trading using deep neural networks on high frequency data
- Single-cell mass cytometry on peripheral blood identifies immune cell subsets associated with primary biliary cholangitis
- Algorithmic trading using deep neural networks on high frequency data
- Single-cell mass cytometry on peripheral blood identifies immune cell subsets associated with primary biliary cholangitis
- Single-cell mass cytometry on peripheral blood identifies immune cell subsets associated with primary biliary cholangitis
- Algorithmic trading using deep neural networks on high frequency data
- Single-cell mass cytometry on peripheral blood identifies immune cell subsets associated with primary biliary cholangitis
- Algorithmic trading using deep neural networks on high frequency data
- Understanding random forests: from theory to practice
- Single-cell mass cytometry on peripheral blood identifies immune cell subsets associated with primary biliary cholangitis
- Algorithmic trading using deep neural networks on high frequency data
- Single-cell mass cytometry on peripheral blood identifies immune cell subsets associated with primary biliary cholangitis
- Algorithmic trading using deep neural networks on high frequency data
- Single-cell mass cytometry on peripheral blood identifies immune cell subsets associated with primary biliary cholangitis
- Algorithmic trading using deep neural networks on high frequency data
- Generic online animal activity recognition on collar tags
- Robust and flexible estimation of data-dependent stochastic mediation effects: a proposed method and example in a randomized trial setting
- Single-cell mass cytometry on peripheral blood identifies immune cell subsets associated with primary biliary cholangitis
- Prospective validation of a transcriptomic severity classifier among patients with suspected acute infection and sepsis in the emergency department - Wiesenecker, Dana Lehmann, Natallia Markmann, Wilma H Bergström, James Wacker, Oliver Liesenfeld, Michael Mayhew, Ljubomir Buturovic, Roland Luethy, Timothy E Sweeney , Rudolf Tauber, Kai Kappert, Rajan Somasundaram, Wolfgang Bauer. (2022)
- Algorithmic trading using deep neural networks on high frequency data
- Single-cell mass cytometry on peripheral blood identifies immune cell subsets associated with primary biliary cholangitis
- Algorithmic trading using deep neural networks on high frequency data
- Single-cell mass cytometry on peripheral blood identifies immune cell subsets associated with primary biliary cholangitis
- Algorithmic trading using deep neural networks on high frequency data
- Single-cell mass cytometry on peripheral blood identifies immune cell subsets associated with primary biliary cholangitis
- Algorithmic trading using deep neural networks on high frequency data
- Single-cell mass cytometry on peripheral blood identifies immune cell subsets associated with primary biliary cholangitis
- Algorithmic trading using deep neural networks on high frequency data
- Single-cell mass cytometry on peripheral blood identifies immune cell subsets associated with primary biliary cholangitis
- Algorithmic trading using deep neural networks on high frequency data
- Algorithmic trading using deep neural networks on high frequency data
- Single-cell mass cytometry on peripheral blood identifies immune cell subsets associated with primary biliary cholangitis
- Algorithmic trading using deep neural networks on high frequency data
- Single-cell mass cytometry on peripheral blood identifies immune cell subsets associated with primary biliary cholangitis
- Algorithmic trading using deep neural networks on high frequency data
- Single-cell mass cytometry on peripheral blood identifies immune cell subsets associated with primary biliary cholangitis
- Single-cell mass cytometry on peripheral blood identifies immune cell subsets associated with primary biliary cholangitis
- Algorithmic trading using deep neural networks on high frequency data
- Generic online animal activity recognition on collar tags
- Algorithmic trading using deep neural networks on high frequency data
- Single-cell mass cytometry on peripheral blood identifies immune cell subsets associated with primary biliary cholangitis
- Single-cell mass cytometry on peripheral blood identifies immune cell subsets associated with primary biliary cholangitis
- Algorithmic trading using deep neural networks on high frequency data
- Single-cell mass cytometry on peripheral blood identifies immune cell subsets associated with primary biliary cholangitis
- Algorithmic trading using deep neural networks on high frequency data
- Inferring passenger types from commuter eigentravel matrices
- Deep learning and association rule mining for predicting drug response in cancer
- Single-cell mass cytometry on peripheral blood identifies immune cell subsets associated with primary biliary cholangitis
- Algorithmic trading using deep neural networks on high frequency data
- Single-cell mass cytometry on peripheral blood identifies immune cell subsets associated with primary biliary cholangitis
- Algorithmic trading using deep neural networks on high frequency data
- Single-cell mass cytometry on peripheral blood identifies immune cell subsets associated with primary biliary cholangitis
- Algorithmic trading using deep neural networks on high frequency data
- Single-cell mass cytometry on peripheral blood identifies immune cell subsets associated with primary biliary cholangitis
- Algorithmic trading using deep neural networks on high frequency data
- Single-cell mass cytometry on peripheral blood identifies immune cell subsets associated with primary biliary cholangitis
- Algorithmic trading using deep neural networks on high frequency data
- Single-cell mass cytometry on peripheral blood identifies immune cell subsets associated with primary biliary cholangitis
- Algorithmic trading using deep neural networks on high frequency data
- Single-cell mass cytometry on peripheral blood identifies immune cell subsets associated with primary biliary cholangitis
- Algorithmic trading using deep neural networks on high frequency data
- Single-cell mass cytometry on peripheral blood identifies immune cell subsets associated with primary biliary cholangitis
- Algorithmic trading using deep neural networks on high frequency data
- Single-cell mass cytometry on peripheral blood identifies immune cell subsets associated with primary biliary cholangitis
- Algorithmic trading using deep neural networks on high frequency data
- Single-cell mass cytometry on peripheral blood identifies immune cell subsets associated with primary biliary cholangitis
- Algorithmic trading using deep neural networks on high frequency data
- Single-cell mass cytometry on peripheral blood identifies immune cell subsets associated with primary biliary cholangitis
- Algorithmic trading using deep neural networks on high frequency data
- Single-cell mass cytometry on peripheral blood identifies immune cell subsets associated with primary biliary cholangitis
- Algorithmic trading using deep neural networks on high frequency data
- Single-cell mass cytometry on peripheral blood identifies immune cell subsets associated with primary biliary cholangitis
- Algorithmic trading using deep neural networks on high frequency data
- Single-cell mass cytometry on peripheral blood identifies immune cell subsets associated with primary biliary cholangitis
- Single-cell mass cytometry on peripheral blood identifies immune cell subsets associated with primary biliary cholangitis
- Algorithmic trading using deep neural networks on high frequency data
- Algorithmic trading using deep neural networks on high frequency data
- Single-cell mass cytometry on peripheral blood identifies immune cell subsets associated with primary biliary cholangitis
- Algorithmic trading using deep neural networks on high frequency data
- Single-cell mass cytometry on peripheral blood identifies immune cell subsets associated with primary biliary cholangitis
- Algorithmic trading using deep neural networks on high frequency data
- Single-cell mass cytometry on peripheral blood identifies immune cell subsets associated with primary biliary cholangitis
- Algorithmic trading using deep neural networks on high frequency data
- Single-cell mass cytometry on peripheral blood identifies immune cell subsets associated with primary biliary cholangitis
- Algorithmic trading using deep neural networks on high frequency data
- Single-cell mass cytometry on peripheral blood identifies immune cell subsets associated with primary biliary cholangitis
- Algorithmic trading using deep neural networks on high frequency data
- Single-cell mass cytometry on peripheral blood identifies immune cell subsets associated with primary biliary cholangitis
- Algorithmic trading using deep neural networks on high frequency data
- Single-cell mass cytometry on peripheral blood identifies immune cell subsets associated with primary biliary cholangitis
- Algorithmic trading using deep neural networks on high frequency data
- Single-cell mass cytometry on peripheral blood identifies immune cell subsets associated with primary biliary cholangitis
- Algorithmic trading using deep neural networks on high frequency data
- Single-cell mass cytometry on peripheral blood identifies immune cell subsets associated with primary biliary cholangitis
- Algorithmic trading using deep neural networks on high frequency data
- Single-cell mass cytometry on peripheral blood identifies immune cell subsets associated with primary biliary cholangitis
- Algorithmic trading using deep neural networks on high frequency data
- Machine Learning-based Meal Detection Using Continuous Glucose Monitoring on Healthy Participants: An Objective Measure of Participant Compliance to Protocol - kyung Woodbridge, Jean L. Fry. (2021)
- Single-cell mass cytometry on peripheral blood identifies immune cell subsets associated with primary biliary cholangitis
- Algorithmic trading using deep neural networks on high frequency data
- Algorithmic trading using deep neural networks on high frequency data
- Single-cell mass cytometry on peripheral blood identifies immune cell subsets associated with primary biliary cholangitis
- Single-cell mass cytometry on peripheral blood identifies immune cell subsets associated with primary biliary cholangitis
- Algorithmic trading using deep neural networks on high frequency data
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Books
- Big data in psychiatry and neurology, Chapter 11: A scalable medication intake monitoring system - Kyung Woodbridge and Kevin Bengtson Wong. (2021)
- Hands on Time Series with R
- Mastering Machine Learning with Spark 2.x
- Machine Learning Using R
- Practical Machine Learning with H2O: Powerful, Scalable Techniques for Deep Learning and AI
- Disruptive Analytics
- Computer Age Statistical Inference: Algorithms, Evidence, and Data Science
- R Deep Learning Essentials
- Spark in Action
- Handbook of Big Data
- Computer Age Statistical Inference: Algorithms, Evidence, and Data Science
- R Deep Learning Essentials
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Benchmarks
- Are categorical variables getting lost in your random forests? - Benchmark of categorical encoding schemes and the effect on tree based models (Scikit-learn vs H2O). Oct 28, 2016
- Deep learning in R - Benchmark of open source deep learning packages in R. Mar 7, 2016
- Szilard's machine learning benchmark - Benchmarks of Random Forest, GBM, Deep Learning and GLM implementations in common open source ML frameworks. Jul 3, 2015
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Presentations
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Courses
- University of San Francisco (USF) Distributed Data System Class (MSDS 697) - Master of Science in Data Science Program.
- University of Oslo: Introduction to Automatic and Scalable Machine Learning with H2O and R - Research Bazaar 2019
- University of Cape Town: Analytics Module - Postgraduate Honors Program in Statistical Sciences.
- Coursera: How to Win a Data Science Competition: Learn from Top Kagglers - Advanced Machine Learning Specialization.
- UCLA: Tools in Data Science (STATS 418) - Masters of Applied Statistics Program.
- GWU: Data Mining (Decision Sciences 6279) - Masters of Science in Business Analytics.
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Software
- modeltime.h2o R package
- Evaporate
- splash R package
- h2oparsnip R package
- Spin up PySpark and PySparkling on AWS
- Forecast the US demand for electricity - time [dashboard](https://ramikrispin.github.io/USelectricity/) of the US electricity demand (forecast using H2O GLM)
- h2o3-pam - 3
- h2o3-gapstat - 3
Programming Languages
Categories
Sub Categories
Keywords
machine-learning
5
data-science
4
h2o
4
r
3
big-data
2
clustering
2
distributed
2
h2o-3
2
java
2
multithreading
2
python
2
deep-learning
2
random-forest
1
spark
1
gradient-boosting-machine
1
xgboost
1
data-mining
1
data-visualization
1
image-processing
1
image-recognition
1
sas
1
text-mining
1
open-source
1
kmeans-clustering
1
kmeans
1
gap-statistic
1
datascience
1
unsupervised-learning
1
pam
1
medoids
1
clustering-algorithm
1
timeseries
1
time-series-analysis
1
time-series
1
tidymodels
1
r-package
1
modeltime
1
forecasting
1
forecast
1