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

The set of functions used for time series analysis and in forecasting.

A time-series companion package to healthyR

Time Series Forecasting RShiny dashboard

Manual forecast modelling with regression, ETS and ARIMA models on an example of time-series data.

Machine learning models build on real time data

Application of the ETS model to forecast rainfall patterns. Leveraging time-series analysis techniques, it predicts future rainfall levels by analyzing historical data specifically from Bahwalnagar District, Punjab, Pakistan.

This project aims to predict gold prices using various time series forecasting techniques. The dataset consists of monthly gold futures data over the last ten years. The primary methods used in this analysis include ARIMA, Error Trend Seasonal (ETS) models, and Exponential Smoothing techniques. The forecast horizon is set for the next two years.

Time series forecast of shipping volumes with ARIMA and ETS models.

Time Series Modelling

Time series forecasting using different methods.

Run differential item functioning analysis on items from a multistage computer adaptive test using examinee ability as matching criterion