Using Deep Learning for Demand Forecasting with Amazon SageMaker

Projetos de modelagem e previsão de séries temporal em linguagem Python e linguagem R. Usarei vários modelos de bibliotecas e pacotes usados para tratamento, modelagem e previsão de séries temporais. Falarei um pouco sobre cada uma delas, gerarei a validação e as previsões e, por fim, realizarei a avaliação com a métricas pertinentes.

GluonTS Deep Learning with Modeltime

Time Series Forecasting for the M5 Competition

GPU-Accelerated Deep Learning for Time Series using Modeltime GluonTS (Learning Lab 53). Event sponsors: Saturn Cloud, NVIDIA, & Business Science.

Dataiku DSS plugin to automate time series forecasting with Deep Learning and statistical models 📈

Learn how to train and evaluate time series models with the Gluon TS library, and then deploy them for inference as a Multi-Model Server SageMaker endpoint using a custom SageMaker container.

Sample GluonTS entrypoint scripts for Amazon SageMaker.

multivariate time series

A project implementing and comparing deep learning models (RNN, LSTM, DeepAR) for stock price forecasting. Using TensorFlow, GluonTS, and Darts, this repo demonstrates advanced time series analysis, including probabilistic forecasting to predict MSFT stock data and visualize prediction uncertainty.

An autoregressive forecasting implementation of a LSTM network, NBEATS architecture, ARIMA and SARIMAX regressions, and Autoformer architecture on rupee dollar exchange rates using pytorch, pytorch lightning, pytorch-forecasting, and GluonTS

DeepAR implementation for seasonal influenza cases in German districts

Comprehensive guide to time series forecasting using deep learning techniques, with practical examples and tutorials.

📈 Generate accurate demand forecasts with Python using synthetic data, featuring ARIMA/SARIMA modeling, evaluation metrics, and reproducible visualizations.