Fast AST based code differencing in Python
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Jan 31, 2026 - Python
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SoliDiffy: AST-Based Code Differencing Tool for Solidity Smart Contracts
Library for Variability-Aware Differencing and the Analysis of Edits to Preprocessor-Based Software Product Lines
variability-analysis diffing software-evolution repository-mining software-product-lines differencing variability
Painting a Canvas with IPv6 Addresses
Explains how to use ARIMA model to forecast future production units, enabling informed decision-making and planning in the electric and gas utilities sector.
exploratory-data-analysis industrial-automation grid-search augmented arima-model differencing stationarity mean-square-error mean-absolute-error auto-arima dickey-fuller-test mean-absolute-percentage-error
This is an ARIMA model used for prediction of sales for a particular item.
This project conducts a thorough analysis of weather time series data using diverse statistical and deep learning models. Each model was rigorously applied to the same weather time series data to assess and compare their forecasting accuracy. Detailed results and analyses are provided to delineate the strengths and weaknesses of each approach.
neural-network python3 lstm gru smoothing weather-forecast bidirectional-lstm time-series-analysis moving-average differencing seasonality qqplots learning-rate-scheduling naive-forecasting centered-approach
Hourly Energy Consumption Forecasting
timeseries random-forest linear-regression forecasting differencing xgboost-model datacleaning seasonality sarima-model
Time Series Analysis of CA Temperature Data.
Predicting Monthly Sales of Perrin Freres Champagne by ARIMA & SARIMAX model.
Прогнозирование спроса на такси
python random-forest linear-regression regression pandas xgboost statsmodels time-series-analysis differencing feature-importance seasonality stationarity lag-features dickey-fuller-test time-difference rolling-window-features stats-models pacf-autocorrelation acf-autocorrelation
Airplane passenger forecast using SARIMA Model Acc: (96.63%)
Forecasting_Airline_Passengers_Seats
forecasting arima seasonal forecasting-models rmse timeseries-data sarimax differencing acf-pacf forecasting-time-series pqd rmse-score timeseries-plots seasonal-forecast
ARIMA(1, d, 0) autoregressive integrated time series predictor with sample autocorrelation estimation
time-series mcp forecasting autocorrelation arima autoregressive python-standard-library differencing stationary-series yule-walker agent-skills
Applying the ADfuller test on the Perrin-Freres-monthly-champagne dataset to check whether my data is stationary or not and eventually applying differencing method and creating 23 months extra date, to use it for the Sarimax model to predict sales for the next 23 months on the p,d, q values respectively.
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