The repository provides an in-depth analysis and forecast of a time series dataset as an example and summarizes the mathematical concepts required to have a deeper understanding of Holt-Winter's model. It also contains the implementation and analysis to time series anomaly detection using brutlag algorithm.

Android app testing reaction times during awake brain surgeries

Forecast the Airlines Passengers. Prepare a document for each model explaining how many dummy variables you have created and RMSE value for each model. Finally which model you will use for Forecasting.

This repository showcases projects from the Data Mining course at UNAM, Mexico. It includes analyses of customer behavior, sales transactions, and a sequence-to-sequence model for text generation based on the Harry Potter series, all developed and presented throughout the semester.

In this section, we will estimate airline passengers using time series methods.

In this section, we will examine the Exponential Smoothing Methods in time series analysis.

TimeSeries Analysis-TimeSeries Forecasting-Exponential Smoothing-Arima-Mape Evaluation-Insight Business

Prepare a document for each model explaining how many dummy variables you have created and RMSE value for each model. Finally which model you will use for Forecasting.

Forecast price of basic commodities East Kalimantan using LSTM Algorithm and Exponential Smoothing

Time Series Analysis Intro

Airline Passengers Forecasting

Exploring time series forecasting with the Holt-Winters model.

Industrial Production Index Time Series Forecasting using a range of models including Holt-Winters, ARIMA, SARIMA, LSTMs, and Facebook's Prophet. The project focuses on predicting production trends through model evaluation, tuning, and visualization of forecasted outcomes.

Forecasting Revenue for Kids Retail Store

Forecasting Triple Exponential Smoothing Alpha-Beta-Gamma Using R Programming