Easyregress

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Description: A fully functional web application for performing time series forecasting, ARIMA modeling, and post-estimation diagnostics using Streamlit. The application is equipped with both manual and automatic ARIMA modeling features, and a variety of diagnostic tools to assess model assumptions and forecast accuracy. The application was designed to handle different types of time series data, including the ability to log-transform and difference the data, and visualize results interactively. Key Features & Contributions: Time Series Analysis: Implemented ARIMA (AutoRegressive Integrated Moving Average) models, including manual input of ARIMA parameters and auto ARIMA for automated model selection. Post-Estimation Diagnostics: Integrated statistical tests such as ADF (Augmented Dickey-Fuller) test for stationarity, and various heteroscedasticity, autocorrelation, and normality tests. Custom Visualizations: Developed custom visualizations such as correlograms (ACF/PACF), residual plots, and forecast vs. actual charts using Matplotlib and Seaborn. Interactive User Interface: Created an intuitive user interface using Streamlit, allowing users to upload datasets, perform model diagnostics, and view results in real time. Structural Break Detection: Implemented a Chow Test to detect potential structural breaks in time series data. Streamlined Data Handling: Optimized data preprocessing steps (e.g., logging, differencing, lag creation) to improve model performance and accuracy. Technical Stack: Languages: Python Libraries: Streamlit, Pmdarima, Statsmodels, Scikit-learn, Matplotlib, Seaborn, Pandas, Numpy

Company Details

Employees
1
Industry
Research Services
NAICS
Scientific Research and Development Services
Research and Development in the Physical, Engineering, and Life Sciences
Research and Development in the Physical, Engineering, and Life Sciences (except Nanotechnology and Biotechnology)
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