Production-style electricity demand forecasting with rolling backtesting, per-meter model selection, anomaly detection and CI.
The Repository contains Udacity's Nanodegree Program final project
Time Series Breakdown of Retail Sales
Everything related to statistics, modelling and forecasting
Time series forecasting of Dissolved Oxygen (Oâ‚‚) in the Southern Bug River using ARIMA, SARIMA, Holt-Winters, and Gradient Boosting on 21 years of water quality data. Best model: Holt-Winters with MAPE = 9.01%.
Time Series Analysis and Forecasting with Exponential Smoothing and Holt-Winters in Python
Forecast any date+value CSV with 8 models scored by walk-forward validation against naive baselines — so you learn whether the forecast beats doing nothing. statsmodels in the browser.
Exploring Time Series in R - This is an exploration of time series analysis that includes moving average, holt-winters smoothing, and ARIMA models.
A web application use machine learning method to predict the electricity consumption data
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.
Keras, Tensorflow eager execution layers for exponential smoothing
Automated the process of training time-series data with multiple Machine Learning and Stats Models to output the most accurate forecast result
Holt-Winters Timeseries Forecast
Exploring different ways of time series forecasting and choosing the best to forecast fares for Quahong City urban mobility dataset
Gold price forecasting with Holt-Winters and ARIMA models, including seasonality, autocorrelation, and comparative evaluation.
Benchmarking app for time series forecasting methods.
End-to-End Supply Chain Forecasting pipeline integrating Google BigQuery, Python (Holt-Winters), and Power BI to optimize logistics and predict demand.
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