lag-features

📈Forecasting Total amount of Products using time-series dataset consisting of daily sales data provided by one of the largest Russian software firms📆

Automatic short-term covid-19 spread prediction by countries and Russian regions

Time Series Forecasting Projects – Research-oriented Python portfolio exploring classical and modern forecasting techniques, from Prophet to LightGBM, with EDA, feature engineering, and model evaluation on real-world datasets.

Time series forecasting: ARIMA/SARIMA, Prophet, LSTM networks and ensemble methods along with automated feature engineering for real-time predictive analytics

Time series data, prevalent across diverse domains like economics, finance, meteorology, and science, encompasses various phenomena such as daily sales, stock prices, temperatures, and population growth. We will analyze different datasets to discern patterns and forecast future trends or extract pertinent insights.

Interpretable time-series forecasting on the AirPassengers dataset using ARIMA, XGBoost, LIME, and SHAP

Machine learning project for retail demand forecasting and inventory planning using time-based validation, lag features, rolling-window features, and business error analysis.

Part of the whole COVID-19 Project of RMDS lab

Прогнозирование спроса на такси

Household Energy Consumption Forecasting, an end-to-end time series forecasting pipeline built on 2M+ minute-level records (2006–2010). The project covers exploratory data analysis, temporal feature engineering (Fourier, calendar, and lag features), time-series cross-validation, and XGBoost modeling for predicting hous

Hourly taxi-order forecasting for Sweet Lift Taxi using LightGBM with lag features and rolling means (RMSE 39.7, target ≤ 48).

📈 Create a simple product sales forecast system in Python with Tkinter, featuring custom calculations and different chart visualizations for data insights.