---
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)
```