Developed time series models utilizing Greykite and Neural Prophet on Walmart retail data, with Flask deployment for efficient and scalable access.
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Updated
Jan 27, 2025 - Jupyter Notebook
Developed time series models utilizing Greykite and Neural Prophet on Walmart retail data, with Flask deployment for efficient and scalable access.
Benchmark comparing 6 time series forecasting models — Seasonal Naive, ETS, ARIMA, Prophet, NeuralProphet, and LightGBM — on the same data with a shared evaluation pipeline.
Algoritmo para previsão de séries temporais usando as bibliotecas Prophet e NeuralProphet em Python.
Auto-ARIMA timeseries forecasting in combination with PELT changepoint detection to predict social media viewership performance and identify major changes in performance trends. Models are deployed into a streamlit webapp for analytical functionality.
Optimized demand forecasting using time series modeling with Prophet and NeuralProphet. Includes autoregressive memory, holiday effects, time-aware cross-validation, and hyperparameter tuning. Delivers interpretable, multi-horizon predictions for short-term accuracy and long-term grid planning purposes.
Project Work for TSDB Ph.D. Course (2 ECTS, 2022)
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