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.

Neuro Retail and Optimisation System

Holt-Winters Timeseries Forecast

Times Series Analysis of Daily Climate dataset using traditional methods

Basic Holt Winter Covid-19 Example

P-140 Air Quality forecasting(CO2 emissions) Business Objective: To forecast Co2 levels for an organization so that the organization can follow government norms with respect to Co2 emission levels. Data Set Details: Time parameter and levels of Co2 emission

This repository is B.Tech. major project on COVID-19 Global and India Forecast

Forecast 5 years sales of souvenir data using Holts-winters and ARIMA methods.

Using MS Excel and R, accurately forecasted total core deposit data from a Richmond Bank. The Holt’s Linear Exponential Smoothing had the overall lowest “Quick and Dirty” MAPE (1.2%), the lowest overall Maximum MAPE (3.49%), and consistently more accurate projections for each of the forecast horizons. Overall, the Unaided, Holts Linear Exponenti…

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.

The purpose of this project is to demonstrate the application of three main forecasting functions: single exponential smoothing, double exponential smoothing and Holt-Winters forecasting.

Analysis of different Forecasting techniques on a time series dataset to forecast the number of tourists in Australia in R

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

Airline passenger traffic prediction using time series forecasting techniques

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).

Time Series Forecasting: City and Resort Hotels Bookings forecasting

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

previsão: Suavização Exponencial, Holt-Winters, SARIMA

Statistics projects using R.

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