Popular GitHub Repositories for Seasonality

NeuralProphet: A simple forecasting package

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

Introduction to time series preprocessing and forecasting in Python using AR, MA, ARMA, ARIMA, SARIMA and Prophet model with forecast evaluation.

Stock market prediction on 5 italian companies using VAR model, OLS regressions and LSTM recurrent neural networks over data retrieved from Refinitiv Eikon

Use Facebook Prophet model to forecast Sales including seasonality patterns

A machine learning solution to forecast sales for Rossmann Pharmaceuticals' stores across various cities six weeks in advance. Factors like promotions, competition, holidays, seasonality, and locality are considered for accurate predictions.

This advanced forecasting tool leverages Prophet, ARIMA, SARIMA, and LSTM models to predict daily sales for 32 pizzas and 64 ingredients. With Prophet achieving the lowest MAPE, it ensures accurate demand forecasts, optimized inventory, and efficient purchase planning, reducing waste, preventing stockouts, and enhancing supply chain efficiency.

Sales Forecasting with Time Series Analysis by using ARIMA and SARIMAX.

A comprehensive machine learning project using Facebook's Prophet to forecast future sales. The model utilized historical data and effectively accounted for various factors, including seasonality effects, demand fluctuations, holiday impacts, promotional activities, and competitive influences.

Auto ARIMA Model

GoldVision is a time series forecasting project that predicts gold price trends using Facebook Prophet. The project incorporates custom seasonality and advanced visualization techniques to provide accurate forecasts and insights.

The aim of this project is to predict the sales six weeks ahead across all the stores of the Rossman Pharmaceutical company using Machine and Deep Learning. The different factors affecting the sales are: promotions, competitions, school-state holiday, seasonality, and locality.

Modelo de machine learning con series temporales que predice la cantidad de taxis para la próxima hora.

Time series analysis and forecast interpretation for decision making with Prophet

Prediction of future global land temperature based on accuracy of different models and evaluating which model performs better