Microsoft Finance Time Series Forecasting Framework (FinnTS) is a forecasting package that utilizes cutting-edge time series forecasting and parallelization on the cloud to produce accurate forecasts for financial data.
microsoft
machine-learning
deep-learning
data-science
r
finance
time-series
forecasting
business
finnts
r-package
rstats
Обновлено 2024-10-29 17:51:56 +03:00
Samples, templates and setup guides in order to run demand forecasting in Azure Machine Learning Service and integrate with Dynamics 365 SCM
Обновлено 2024-09-26 16:34:31 +03:00
Utility functions for easier usage of SQL Server Machine Learning Services
Обновлено 2024-07-12 23:52:03 +03:00
Density-based spatial clustering of applications with noise visualization
Обновлено 2024-04-16 11:25:20 +03:00
Anomaly detection analysis and labeling tool, specifically for multiple time series (one time series per category)
Обновлено 2024-02-08 18:29:01 +03:00
Zoom Data Integration with Viva Insights
Обновлено 2023-08-16 17:35:08 +03:00
Find outliers in your data, using a funnel plot
Обновлено 2023-07-08 06:35:09 +03:00
This repo contains a walkthrough of how to use RServer for HDInsight with large data sets like Criteo.
Обновлено 2023-06-27 16:07:15 +03:00
This is a sample R ShinyApp application which shows how to query data from a SQL Azure database in the Microsoft Azure cloud and visualise that data, which contains geospacial coordinates, onto a World Map.
Обновлено 2023-06-27 16:05:21 +03:00
Develop Portable R Code for Use with DeployR
Обновлено 2023-06-14 18:19:32 +03:00
R-powered custom visual implementing the “Seasonal and Trend decomposition using Loess” algorithm, offering several types of plots. Time series decomposition is an essential analytics tool to understand the time series components and to improve forecasting.
Обновлено 2023-06-12 23:28:34 +03:00
This solution template shows how to build and deploy a loan-credit-risk solution with Microsoft ML Server
Обновлено 2023-06-12 23:03:52 +03:00
This solution template demonstrates how to build and deploy a retail online fraud detection solution.
Обновлено 2023-06-12 23:02:25 +03:00
Loan ChargeOff Risk Solution Template with Microsoft ML Server
Обновлено 2023-06-12 23:02:02 +03:00
An R-powered custom visual implementing Autoregressive Integrated Moving Average (ARIMA) modeling for the forecasting. Time series forecasting is the use of a model to predict future values based on previously observed values.
Обновлено 2023-06-12 21:29:06 +03:00
R library for common information retrieval metrics
Обновлено 2023-06-05 13:06:42 +03:00
R-powered custom visual. Based on exponential smoothing time series forecasting
Обновлено 2023-06-02 23:39:02 +03:00
R-powered custom visual. Implements k-means clustering
Обновлено 2023-06-02 23:39:02 +03:00
Find outliers in your data, using the most appropriate method and plot.
Обновлено 2023-06-02 22:13:49 +03:00
Patterns and examples for running R code with Azure Machine Learning
Обновлено 2022-09-16 23:04:07 +03:00
R-powered custom visual implements spline smoothing
Обновлено 2022-09-01 21:14:34 +03:00
R-powered custom visual. Implements assosiation rules mining
Обновлено 2022-09-01 21:14:34 +03:00
Forcasting tbats
Обновлено 2022-08-29 17:02:49 +03:00
A model to merge firm data across datasets utilizing exact & non-exact firm identifiers
Обновлено 2021-08-27 20:01:20 +03:00
Analyzing the safety (311) dataset published by Azure Open Datasets for Chicago, Boston and New York City using SparkR, SParkSQL, Azure Databricks, visualization using ggplot2 and leaflet. Focus is on descriptive analytics, visualization, clustering, time series forecasting and anomaly detection.
azure
data
r
visualization
workshop-materials
anomaly-detection
azure-databricks
databricks-notebooks
timeseries-forecasting
time-series-analysis
sparksql
311-data
aiforsocialgood
anomalydiscovery
datascience-machinelearning
eda
geospatial
leaflet
opendata
sparkr
Обновлено 2021-05-03 23:14:01 +03:00
AzureSMR is no longer being actively developed. For ongoing support of Azure in R, see: https://github.com/Azure/AzureR
Обновлено 2019-07-05 20:56:36 +03:00
A tool for analyzing and visualizing discrete temporal events
Обновлено 2018-08-15 11:24:51 +03:00