Partial port of statsmodels Holt Winters exponential smoothing library using tensorflow for matrix operations and optimization

Trabajo Presentado en el Máster de Big Data, Data Science e IA del tema de Series Temporales

This repository presents a comprehensive analysis of Amazon's revenue forecasting for the fiscal year 2021, analyzing historical revenue data from 2010 to 2020 and identifying the most suitable forecasting model that accurately predicts Amazon's revenue trends considering trend and seasonality.

Airline passenger traffic prediction using time series forecasting techniques

The objective is to forecast Procter & Gamble's stock performance using time series analysis to provide valuable insights for investors and stakeholders.

This project focuses on Time Series Analysis techniques, uncovering patterns and leveraging forecasting models to predict future sales trends.

Keeping Inventory of spare in various service centre to the market demand is always a challenge as most service centres spends significant amount in spare parts inventory costs. In spite of this, availability of spare parts is been one of the problem areas.

Time Series Analysis and Forecasting in Python

Program Exercises in R Language from book: "Forecasting, Time Series and Regression: An Applied Approach" / Ejercicios resueltos en R del libro "Pronosticos, Series de tiempo y Regresión: Un enfoque práctico" de Bruce L. Bowerman, Richard T. O´Connell, Anne B. Koehler, ISBN: 9789706866066 , Cuarta edición, Editorial: Thomson Año 2007

Times Series Analysis of Daily Climate dataset using traditional methods

Neuro Retail and Optimisation System

Holt-Winters method to forecast time series data, evaluating accuracy, and Visualized predictions. Enhanced data-driven decision-making.

Business Problem: Oil price may fluctuate time to time based on more factors technical economical and natural as well as political so the forecasting may not be influenced by these some unexpected scenarios like Geopolitical issues (e.g.: War and Oil price Cap).

An exciting analysis of monthly car sales of a company

Forecasting time series data using Holt Winter's Model in Python

previsão: Suavização Exponencial, Holt-Winters, SARIMA

In first project: revenue prediction for two product categories with the best results on the first income using the ETNA library and in the second income using the Holt Winters method. In Second project: clients classification on potentially ready for churn and not.

Implementation of various Time Series Methods in Python

This project was created using RStudio and used to forecast the future policy sales in a property insurance company using Holt-Winters Triple Exponential Smoothing Additive Model

Time Series Forecasting: City and Resort Hotels Bookings forecasting