tbats

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

A library that unifies the API for most commonly used libraries and modeling techniques for time-series forecasting in the Python ecosystem.

Forecasting building energy demand through time series analysis and machine learning.

Complete solution for MOFC M5 Forecasting in kaggle.

Cambridge UK temperature forecast R models

Identified the most appropriate Time-Series method to forecast drought in African countries, acting as a critical early warning for drought managements

A fable wrapper for TBATS

Knowledge of various Time Series Forecasting topics: Long Short-Term Memory (LSTM), Exponential Smoothing, Autoregressive integrated moving average (ARIMA), TBATS, Multivariate Time Series Forecasting, XGboost, N_BEATS, and Prophet.

Panel time-series forecasting notebooks (daily sales across stores × items). Clean validation (holdout + rolling-origin backtest), strong statistical baselines (SARIMAX/TBATS/ARIMA), and automated models (AutoTS), with optional Prophet/Darts/NeuralProphet. Primary metric: SMAPE.

Time series analysis project: forecasting brazilian inflation.

Predicting Walmart Sales and Performing Exploratory Data Analysis

Application of real-time visualization and forecasting of COVID-19 build on R and shiny

a short program that analyzes time-series of sales to forecast future demand of certain products from a set of stores

Time series analysis project: forecasting M3 competition series.

Forecasting Fixed Rate Mortgage Average in The United States

JAX-native TBATS (forecast::tbats port) — multi-seasonal forecasting with vmap panels on CPU/GPU

This project aims to analyze and forecast daily revenue and the daily number of receipts across six distinct restaurants, by employing a statistical approach and utilizing predictive models, particularly the SARIMA and TBATS models.

STAT497 APPLIED TIME SERIES ANALYSIS term project

A comparative breakdown of traditional econometric timeseries models vs. more modern ML methods for predicting a retail firm's sales over a short to medium horizon

This repository hosts code and models for weather forecasting using TBATS (Trigonometric seasonality, Box-Cox transformation, ARMA errors, Trend, and Seasonal components) models. The project includes data preprocessing, model training, evaluation, and forecasting based on historical daily weather data.