Analysis and Forescasting of Health-Related Time Series Data using ARIMA, Holt Winters and LSTM

I implement a ETS (ANN) and ETS (AAA) model, followed by a Simple Exponential Smoothing, Holt and Holt-Winters model. In conclusion I compare the results and recommend the best alternative.

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

The goal of this project was to apply various quantitative methods, (i.e. Times Series Models and Causal Models) to forecast the sales of the products available in the dataset obtained from Kaggle.

Analyze and forecast time series data on the number of visitors to various Italian museums using techniques such as TimeGPT, SARIMAX, Holt-Winters, Lasso, Gradient Boosting, GAM, etc.

Time Series Analysis and Forecasting with Exponential Smoothing and Holt-Winters in Python

Data Science - Forecasting

Predicting Stock Prices Using Forecasting Methods merupakan analisa forecasting yang dilakukan untuk salah satu emiten saham di bursa saham amerika pada tahun 2012-2013 dan melakukan berbagai macam eksperimentasi model untuk meminimalkan MAE

Project on Foreign Exchange Forecasting, for the Μ401 - Deep Neural Networks course, NKUA, Fall 2022.

As a project for a course in Machine Learning applications on time series analysis, I created some functions to work with time series data (different frequencies) coming from the Central Reserve Bank of Peru (BCRP).

Forecasting the daily demand for lettuce in four individual restaurants of a major US fast-food chain.

Previsões de acessos ao blog Cinetour - Viagem de Cinema

Personal research for bachelor's thesis performed into the Holt Winters model and its components for Time Series Forecasting.

TimeSeries Analysis in R

Forecast the lettuce demand for four US-based branches of a fast-food restaurant chain to support inventory replenishment decisions.

A comparison of time-series forecasting models on a weekday-only data using StatsForecast library.

This project aims to built sales forecast webapps for Category A, Category B, Product A1, Product A2, Product B1 & Product B2

Forecast the Airlines Passengers and CocaCola Prices data set. Prepare a document for model explaining. How many dummy variables you have created and RMSE value for model. Finally which model you will use for Forecasting.

This is a sub-system of Modular Platform for Web Pentests. Observer provides an easily extendable infrastructure for detecting cyber attacks. It contains several primary modules that include input acceptance, input analysis and a reporting tool. As an example, the identification of DoS and ICMP flood threats has been implemented.