https://github.com/tymill/synthpred
A Julia package for synthetic data analysis, advanced imputation (ARIMA, RNN), AutoML, and ensemble modeling.
https://github.com/tymill/synthpred
arima automl ensemble flux imputation julia machine-learning synthetic-data time-series
Last synced: 6 months ago
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A Julia package for synthetic data analysis, advanced imputation (ARIMA, RNN), AutoML, and ensemble modeling.
- Host: GitHub
- URL: https://github.com/tymill/synthpred
- Owner: TyMill
- License: mit
- Created: 2025-03-26T12:13:52.000Z (7 months ago)
- Default Branch: main
- Last Pushed: 2025-04-16T11:40:12.000Z (6 months ago)
- Last Synced: 2025-04-22T12:16:47.469Z (6 months ago)
- Topics: arima, automl, ensemble, flux, imputation, julia, machine-learning, synthetic-data, time-series
- Language: Julia
- Homepage:
- Size: 308 KB
- Stars: 2
- Watchers: 1
- Forks: 2
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
- Citation: CITATION.bib
Awesome Lists containing this project
README
# SynthPred.jl
[](https://tymill.github.io/SynthPred/)
[](https://doi.org/10.5281/zenodo.15090892)
**SynthPred.jl** is a Julia package for synthetic data analysis, advanced imputation (ARIMA, RNN), AutoML, and ensemble modeling.
---
## 🚀 Features
- 🔍 Descriptive statistics and missing data reporting
- 🧼 Simple and advanced imputation:
- Mean, median, mode
- Forward/backward fill
- Gaussian distribution sampling
- Time series-based: ARIMA
- Sequence learning-based: RNN (Flux.jl)
- 🤖 AutoML for classification (MLJ.jl-based)
- ⚖️ Blending top-performing models via ensembling
- 📊 Predictions on new data
- 📑 JSON/CSV imputation reports
---
## 📦 Installation
```julia
using Pkg
Pkg.add(url="https://github.com/TyMill/SynthPred.jl")
```
---
## 🧪 Quick Example
```julia
using SynthPred
using CSV, DataFrames
# Load training data
df = CSV.read("data/example.csv", DataFrame)
# Explore data
SynthPred.Exploration.describe_data(df)
# Impute missing values (e.g. RNN strategy)
df_clean, report = SynthPred.Imputer.impute_advanced(df, "rnn", threshold=0.1)
SynthPred.Imputer.save_imputation_report(report, "reports/imputation_report.json")
# Run AutoML pipeline
top_models, scores = SynthPred.AutoML.run_automl(df_clean, :target)
X = select(df_clean, Not(:target))
y = df_clean[:, :target]
ensemble = SynthPred.AutoML.blend_top_models(top_models, X, y)
# Predict on new data
Xnew = CSV.read("data/new_data.csv", DataFrame)
preds = SynthPred.AutoML.predict_ensemble(ensemble, Xnew)
println(preds)
```
---
## 📚 Documentation
Full documentation is available at: [https://your-username.github.io/SynthPred.jl](https://tymill.github.io/SynthPred.jl)
---
## 🧪 Project Structure
```
SynthPred/
├── Project.toml
├── src/
│ ├── SynthPred.jl
│ ├── Exploration.jl
│ ├── Imputer.jl
│ └── AutoML.jl
├── data/
│ ├── example.csv
│ └── new_data.csv
├── reports/
│ └── imputation_report.json
├── docs/
│ └── src/index.md
├── test/
│ └── runtests.jl
└── main.jl
```
---
## 📌 Roadmap
- [x] Core modules: Exploration, Imputer, AutoML
- [x] ARIMA and RNN-based imputations
- [x] AutoML + model blending with MLJ.jl
- [x] Imputation reports (CSV/JSON)
- [x] Documentation (Documenter.jl + GitHub Pages)
- [ ] Exporting trained models (`JLD2`, `BSON`)
- [ ] Web GUI with Pluto.jl or Dash.jl
- [ ] Integration with JuliaHub and Zenodo DOI
---
## 🤝 Contributing
Pull requests are welcome! For major changes, please open an issue first to discuss your proposal.
---
## 📜 License
MIT License © 2025 Tymoteusz Miller
---
## 📬 Contact
📧 me@tymoteuszmiller.dev
---
Built with ❤️ in Julia for real-world ML and scientific discovery.