Modeltime unlocks time series forecast models and machine learning in one framework

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.

Experimental R interface for ReservoirPy

Anomaly Detection in R - the tidy way using anomalize

AI Forecasting tool for Time-Series and Non-time series data

Time series prediction system in R (RStudio) for given real-time e-commerce dataset of thousands of products, customers, and categories with the help of data mining algorithms (ARIMA, Holt Winter, STL, ETS).

Shiny app for FSN model comparison

Weather time series data forecasting using Neural network autoregressive and Fourier-Autoregressive Moving Average

Mechanistic Bayesian Machine Learning model of eczema dynamic

Bayesian analysis and forecasting of Bitcoin volatility. Definition of GARCH and ARCH models through MCMC sampling.

A shiny web application template for advanced interactive and predictive data analytics for time series.

R package providing models to serve as building blocks for predicting eczema severity

efor: Easy Forecasts, a package assisting in creating forecasts for multiple articles.

RShiny dashboard for Visualization & Forecasting of time series data

ARIMA model for forecasting daily Covid-19 cases in Morocco

Natural Gas Demand Forecast Dashboard

Practicum for MS in Data Science, Regis University, Summer 2020

Machine Learning project to aproximate trend changes in forex environment using timeseries-forecasting methods: EURUSD 1h candles as a dataset.

This project analyzes the growth of an emerging artist on Spotify, examining data from January 2021 to October 2023. We explore key factors such as streams, playlist reach, and follower counts using time series forecasting m

Predicting eczema severity with biomarkers using a Bayesian state-space model