Time Series Forecasting with Machine Learning
Skforecast, a Python library that simplifies the use of scikit-learn models for forecasting and time series problems.
ARIMA and SARIMAX Models
ARIMA and SARIMAX models for time series forecasting.
Time Series Forecasting with Gradient Boosting: XGBoost, LightGBM, and CatBoost
Using gradient boosting models to forecast the number of users of an urban bike-sharing system.
Electricity Demand Forecasting with Machine Learning
An example of predicting electricity demand using machine learning models.
Global Forecasting Models I: Modeling Multiple Time Series with Machine Learning
Forecasting models for predicting multiple time series simultaneously.
Global Forecasting Models II: Comparative Analysis of Single and Multi-Series Forecasting Modeling
Benckmark to compare the forecast results of the global models with the models of individual series
Global Forecasting Models III: Scalable Forecasting
Modeling thousand time series with a single global model
Global Forecasting Models IV: The M5 Accuracy competition
The success of global forecasting models in forecasting competitions
Forecasting with Deep Learning Models
Forecasting using neural network-based models such as RNN and LSTM.
Probabilistic Forecasting I: prediction intervals
Methods for estimating prediction intervals for machine learning models applied to forecasting problems.
Probabilistic Forecasting II: Prediction Intervals in Multi-Step Forecasting
Prediction intervals for multi-step time series forecasting.
Forecasting time series with missing values
Strategies for modelling and forecasting incomplete time series.
Intermittent Demand Forecasting
Predicting intermittent demand using machine learning models.
Web Traffic Forecasting
An example of predicting web traffic using machine learning models.
Bitcoin Price Prediction
An example of using forecasting models to predict the price of Bitcoin and studying implications when a time series lacks any pattern.
Reducing the Impact of COVID-19 in Forecasting Models
An example of mitigating the impact of COVID-19 and other anomalies in time series when training forecasting models.
Interpretability and Explanability in Forecasting Models
Interpretability of forecasting models using SHAP values, partial dependence plots, and feature importance.
Forecasting trend with tree based models
Modelling time series with trend using tree based models.
Stacking ensemble of forecasting models
Stacking ensemble of machine learning models to improve predictions.
Time series anomaly detection
Detection of anomalies and outliers in time series using forecasting models.
Data leakage in pre-trained forecasting models
Data leakage and overfitting problems in pre-trained forecasting models.
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