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.

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

Cambridge UK temperature forecast R models

A fable wrapper for TBATS

Time series analysis project: forecasting brazilian inflation.

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

Study of time-frequency representations in the presence of heteroscedastic dependent noise

Time series analysis project: forecasting M3 competition series.

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.

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.

Predicting Walmart Sales and Performing Exploratory Data Analysis

2021 Amirkabir Artificial Intelligence Competitions (AAIC): Challenge of forecasting daily internet usage of MCI subscribers

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.