Lightning ⚡️ fast forecasting with statistical and econometric models.

A Python package for Bayesian forecasting with object-oriented design and probabilistic models under the hood.

StateSpaceModels.jl is a Julia package for time-series analysis using state-space models.

PyTorch code for ETSformer: Exponential Smoothing Transformers for Time-series Forecasting

Hierarchical Time Series Forecasting with a familiar API

Implementation of ETSformer, state of the art time-series Transformer, in Pytorch

Book and material for the course "Time series analysis with Python" (STA-2003)

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

Forecasted product sales using time series models such as Holt-Winters, SARIMA and causal methods, e.g. Regression. Evaluated performance of models using forecasting metrics such as, MAE, RMSE, MAPE and concluded that Linear Regression model produced the best MAPE in comparison to other models

Real-time time series prediction library with standalone server

Forecasting Monthly Sales of French Champagne - Perrin Freres

Time Series Forecasting Methods — A collection of Python implementations for essential time series forecasting techniques, including Simple, Double, Triple Exponential Smoothing, and Moving Averages.

Exponential Smoothing, SARIMA, Facebook Prophet

Forecasting Time Series with Moving Average and Exponential Smoothing

Borealis AI mentored water consumption prediction machine learning web application!

Holt-Winters exponential smoothing implemented in Go.

Theta methods for time series forcasting

Brazilian PIB (GDP) time series analysis.

The Korea National Oil Corporation was interested in purchasing shale gas wells from the United States and wanted to predict their production to select wells that maximize profit.