Fast AST based code differencing in Python
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Jan 14, 2025 - Python
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differencing
Here are 13 public repositories matching this topic...
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
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Jan 12, 2025 - Java
SoliDiffy: AST-Based Code Differencing Tool for Solidity Smart Contracts
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Oct 13, 2024 - Jupyter Notebook
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
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Jan 8, 2025 - Jupyter Notebook
Painting a Canvas with IPv6 Addresses
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May 27, 2023 - Python
This is an ARIMA model used for prediction of sales for a particular item.
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Dec 15, 2023 - Jupyter Notebook
Hourly Energy Consumption Forecasting
timeseries random-forest linear-regression forecasting differencing xgboost-model datacleaning seasonality sarima-model
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Jan 30, 2023 - Jupyter Notebook
mega premium downloads is free
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Dec 22, 2024
Predicting Monthly Sales of Perrin Freres Champagne by ARIMA & SARIMAX model.
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Aug 23, 2022 - Jupyter Notebook
Time Series Analysis of CA Temperature Data.
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Jul 25, 2022
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
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Nov 7, 2022 - Jupyter Notebook
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
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Aug 29, 2022 - Jupyter Notebook
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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Mar 9, 2021 - Jupyter Notebook
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