[BA project] Dynamic Pricing Optimization for Airbnb listing to optimize yearly profit for host. Use Clustering for competitive analysis, kNN regression for demand forecasting, and find dynamic optimal price with Optimization model.

Building Time series forecasting models, including the XGboost Regressor, GRU (Gated Recurrent Unit), LSTM (Long Short-Term Memory), CNN (Convolutional Neural Network), CNN-LSTM, and LSTM-Attention. Additionally, hybrid models like GRU-XGBoost and LSTM-Attention-XGBoost for Electricity Demand and price prediction

Forecasts next 24 hours of hourly energy demand with Keras, Prophet, and SARIMA (statsmodels)

Forecast the US demand for electricity

[AAAI'19] Spatiotemporal Multi-Graph Convolution Network for Ride-Hailing Demand Forecasting (Pytorch Replication)

Demand Forecasting Models for Kaggle competition

Using Deep Learning for Demand Forecasting with Amazon SageMaker

Full-stack Highly Scalable Cloud-native Machine Learning system for demand forecasting with realtime data streaming, inference, retraining loop, and more

This project forecasts renewable energy demand using LSTM-based time series models. It processes historical demand data, trains predictive models, and visualizes future trends, enabling better planning and management

This project aims to build accurate and scalable demand forecasting models for e-commerce and retail businesses

Model to accurately forecast inventory demand based on historical sales data.

Electricity price (energy demand) forecasting using different ML, DL, stacked DL and hybrid methods (XGBoost, GRU, LSTM, CNN, CNN-LSTM, LSTM-Attention, Hybrid GRU-XGBoost and Hybrid LSTM-Attention-XGBoost)..

Machine learning models applied to water demand forecasting in the City of London.

This project applies time series forecasting (ARIMA) to predict product demand and optimize inventory management. By analyzing historical demand and stock levels, it calculates reorder points, safety stock, and optimal order quantities to minimize costs, prevent stockouts, and improve service efficiency.

Electricity demand forecasting for Austin, TX, using a combination of timeseries methods and regression models

BASOPRA - BAttery Schedule OPtimizer for Residential Applications. Daily battery schedule optimizer (i.e. 24 h optimization framework), assuming perfect day-ahead forecast of the electricity demand load and solar PV generation in order to determine the maximum economic potential regardless of the forecast strategy used. Include the use of different applications which residential batteries can perform from a consumer perspective. Applications such as avoidance of PV curtailment, demand load-shifting and demand peak shaving are considered along with the base application, PV self-consumption. Different battery technologies and sizes can be analyzed as well as different tariff structures. Aging is treated as an exogenous parameter, calculated on daily basis and is not subject of optimization. Data with 15-minute temporal resolution are used for simulations. The model objective function have two components, the energy-based and the power-based component, as the tariff structure depends on the applications considered, a boolean parameter activate the power-based factor of the bill when is necessary.

Using an integrated pinball-loss objective function in various recurrent based deep learning architectures made with keras to simultaneously produce probabilistic forecasts for UK wind, solar, demand and price forecasts.

AI-driven demand-side energy management optimizes how consumers use electricity by forecasting demand, shifting loads, and automating energy-intensive processes. It reduces peak consumption, lowers costs, and enhances grid stability.

End-to-end automated pipeline in Python that forecasts weekly demand for products & recommends corresponding optimal prices for a retail chain (Machine Learning in sklearn, MIP optimization in Gurobi)

物流需求预测法的python实现（Logistics Demand Forecasting By Python），含移动平均法、指数平滑法、平滑系数的确认、结果输出到excel表、误差分析等

Using machine learning to solve one of the most common problem of Supply Chain domain, i.e Demand Forecasting.

⚽ Bike ⚾ Sharing 🥎 Demand 🏀Forecasting 🏐 Time 🎮 Series 🥌 Analysis is 🎳 a data ⛸ science ✈ focused on 🚁 predicting 🚀 bike 🛸 demand 🚟 time 🚠 series 🚞 techniques ⛴ analyzing 🚢 historical 🚒 bike 🛺 weather 🚋 data 🚂 seasonal 🚃 trends this 🚅 helps 🏩 optimize 🏦 planning 🕍 resource 🏠 allocation 🕌 and 🔐 operational 🪣 efficiency 💶

Forecasting Uber demand in NYC neighborhoods

Time-series demand forecasting is constructed by using LSTM, GRU, LSTM with seq2seq architecture, and prophet models.

KDD'2025. This project aims to explore the application of hypergraph spatio-temporal learning and Large Language Models (LLMs) in traffic flow demand forecasting

Time series regression models using ARIMA, SARIMAX, and Recursive Neural Network to predict day-ahead and hour-ahead California wholesale electricity prices. Features include demand forecasts, NOAA weather station data, and CA Dept. of Water Resources reservoir water level hourly observation.

🚃 Promotion ✈ Sensitive 🚁 Hierarchical 🛸Probabilistic 🛼 Forecasting ⛱ Demand is 🕌 an advanced 🏡 machine ⚽ learning 🏦 framework 🏟 designed to ⚾ accurately 🥎 forecast 🏀 Consumer 🏐 demand by 📔 capturing 📕 promotional 📗 effects 📘 hierarchical 📙 sales 🪣 dependencies 🛁 uncertainty 🧺 distributions 🪬 across products regions time scales

Implementation of STG2Seq: Spatial-temporal Graph to Sequence Model for Multi-step Passenger Demand Forecasting

This machine learning model (LSTM Time Series model) helps us to forecast demand of a supply chain business problem. This model uses Keras which uses tensorflow to solve the problem.

GluonTS Implementation of Intermittent Demand Forecasting with Deep Renewal Processes arXiv:1911.10416v1 [cs.LG]