NeuralProphet: A simple forecasting package

ML powered analytics engine for outlier detection and root cause analysis.

Forecasting

A web application that makes it possible to analyse time-series data. Using techniques for seasonality and trend detection and Granger causality

Shifts gears through various data science techniques to analyze and visualize bike sharing trends, using the UCI Bike Sharing Dataset.

Predictions on energy prices across EU countries

Time series forecasting using ML forecasting models

Walker et al. (2022) Mycobacterium Tuberculosis Transmission in Birmingham, UK, 2009–19. Lancet Reg Health Europe. 17: 100361 https://doi.org/10.1016/j.lanepe.2022.100361

Multispecies occupancy models to investigate seasonal co-occurrence of predator-prey pairs and changes withing these co-occurrences.

Time-series analysis of climate change data

Analysis of seasonal habitat-use patterns of Aldabra giant tortoises of Aldabra Atoll, Seychelles using temporary emigration models.

This project delves into Indonesia's dynamic hospitality industry, uncovering seasonal booking trends, the impact of stay duration on cancellations, and how lead time affects cancellation rates. Valuable insights guide hotels to optimize pricing, policies, and customer engagement, enhancing competitiveness in the Indonesian hospitality sector.

💹 seasonal patterns to GROW REVENUE • 2023

A python library for Bayesian time series modeling

Capstone project for SpringBoard AI/ML Certification

Seasonal survival analysis of the gray mouse lemur population in Kirindy Forest, Madagascar using multistate capture-recapture models.

An open-source package written in Julia for seasonal plants epidemics

Using ICESAT-2 to detect topographical changes over land surfaces, in particular permafrost

An end-to-end code solution involves performing EDA and sales prediction using time series analysis in R with ML models and evaluating their performance based on accuracy metrics.

This project uses time series forecasting to predict future milk production. The data used in this project is monthly milk production data from January 1962 to December 1975. The ARIMA (autoregressive integrated moving average) model is used to forecast the milk production. The model is evaluated using various metric.