Create README.md

README.md

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+---
+language: en
+tags:
+- time-series-forecasting
+- finance
+- lstm
+- keras
+---
+# Time-Series Forecasting for Virtual Item Prices
+
+This repository contains trained machine learning and deep learning models for forecasting the price direction of virtual assets based on time-series data.
+
+## Best Model
+The best performance was achieved by the **LSTM** neural network on the `Kneedle (MAE <= 0.416)` data pool, yielding a metric of **ROC-AUC = 0.6567**.
+
+**Final model architecture:**
+* `LSTM(128)` – recurrent layer, tanh activation
+* `Dropout(0.3)`
+* `Dense(64, ReLU)`
+* `Dense(1, sigmoid)` – probability of price increase
+
+## Repository Structure
+* `lstm_final_kneedle.keras` — the main optimized model.
+* `lstm_final_kneedle_cosmetics.keras` — model for the alternative dataset (appendix).
+* `best_experiments/` — the best models across 14 different algorithms (CatBoost, XGBoost, TabNet, etc.) after pool optimization.
+
+## Benchmarking
+
+| Model | Best Pool | ROC-AUC |
+|:---|:---|:---|
+| **LSTM** | **Kneedle (MAE ≤ 0.416)** | **0.6567** |
+| CatBoost | RF Gating (t = 0.7) | 0.6446 |
+| XGBoost | ENN (t = 0.7) | 0.6405 |
+| TabNet | RF Gating (t = 0.7) | 0.6398 |
+| LightGBM | DROP3 (t = 0.7) | 0.6339 |
+| Decision Tree | RF Gating (t = 0.7) | 0.6296 |
+| Random Forest | RF Gating (t = 0.7) | 0.6294 |
+| MLP | ENN (t = 0.7) | 0.6292 |
+| Logistic Regression| DT Filter (t = 0.5) | 0.6235 |
+| SVM | ENN (t = 0.7) | 0.6178 |
+| KNN | Kneedle (MAE ≤ 0.416) | 0.5844 |
+| Naive Bayes | DT Filter (t = 0.5) | 0.5731 |
+| 1D-CNN | ENN (t = 0.7) | 0.5367 |
+| AutoARIMA | DT Filter (t = 0.7) | 0.5027 |
+
+## Inference
+
+```python
+from huggingface_hub import hf_hub_download
+from tensorflow.keras.models import load_model
+
+# Load the best model
+model_path = hf_hub_download(
+    repo_id="redr1g/final-thesis-experiments", 
+    filename="lstm_final_kneedle.keras"
+)
+model = load_model(model_path)
+```