README.md

---
license: mit
---

# Apple Stock Price Forecasting

This repository contains models for forecasting Apple stock prices using ARIMA and LSTM.

## Inference Instructions

You can either navigate to the specific model folder and open the provided notebook, or run the inference code directly below.

---

<details>
<summary>ARIMA Model Inference</summary>

```python
# Install required packages
!pip install --quiet yfinance joblib pmdarima huggingface_hub

# Import Libraries
from huggingface_hub import hf_hub_download
import joblib
import numpy as np
import pandas as pd
import yfinance as yf

HF_TOKEN = "your_own_hf_token"

# Load ARIMA model and Box-Cox transformer
arima_model_path = hf_hub_download(
    repo_id="EsferSami/DataSynthis_ML_JobTask",
    filename="Apple-Stock-Price-Forecasting-ARIMA-Model/apple_stock_arima.pkl",
    token=HF_TOKEN
)
bct_path = hf_hub_download(
    repo_id="EsferSami/DataSynthis_ML_JobTask",
    filename="Apple-Stock-Price-Forecasting-ARIMA-Model/boxcox_transformer.pkl",
    token=HF_TOKEN
)

arima_model = joblib.load(arima_model_path)
bct = joblib.load(bct_path)

# Download recent data
data = yf.download("AAPL", period="3mo", auto_adjust=False)
recent_prices = data['Adj Close'].values.astype(float)

# Transform and forecast
y_trans, _ = bct.transform(recent_prices)
resid_std = np.std(arima_model.resid()) if hasattr(arima_model, "resid") else np.std(y_trans - np.mean(y_trans))

predictions_trans = []
current_series = y_trans.copy()
for day in range(7):
    try:
        pred = arima_model.predict(n_periods=1)[0]
    except Exception:
        pred = current_series[-1]
    pred = current_series[-1] + np.random.normal(0.0, resid_std*0.3)
    predictions_trans.append(pred)
    current_series = np.append(current_series, pred)

predictions_price, _ = bct.inverse_transform(np.array(predictions_trans))
prediction_dates = pd.date_range(start=data.index[-1] + pd.Timedelta(days=1), periods=7)
arima_results_df = pd.DataFrame({'Date': prediction_dates, 'Predicted_Price': predictions_price})

print("\nARIMA - 7-Day Forecast")
print("="*60)
print(arima_results_df.to_string(index=False))

# Install required packages
!pip install --quiet yfinance joblib tensorflow huggingface_hub scikit-learn

# Import Libraries
from huggingface_hub import hf_hub_download
import tensorflow as tf
import joblib
import numpy as np
import pandas as pd
import yfinance as yf
from sklearn.preprocessing import MinMaxScaler

HF_TOKEN = "your_own_hf_token"

# Load model and scaler
model_path = hf_hub_download(
    repo_id="EsferSami/DataSynthis_ML_JobTask",
    filename="Apple-Stock-Price-Forecasting-LSTM-Model/apple_stock_lstm.h5",
    token=HF_TOKEN
)
scaler_path = hf_hub_download(
    repo_id="EsferSami/DataSynthis_ML_JobTask",
    filename="Apple-Stock-Price-Forecasting-LSTM-Model/scaler.joblib",
    token=HF_TOKEN
)

model = tf.keras.models.load_model(model_path)
scaler = joblib.load(scaler_path)

# Download recent data
data = yf.download("AAPL", period="3mo", auto_adjust=False)
recent_prices = data['Adj Close'].values.astype(float)

# Prepare input
last_60_days = recent_prices[-60:].reshape(-1, 1)
last_60_scaled = scaler.transform(last_60_days)

predictions = []
current_seq = last_60_scaled.copy()
last_price = last_60_days[-1][0]
MAX_DAILY_CHANGE = 0.02

for day in range(7):
    input_data = current_seq.reshape(1, 60, 1)
    pred_scaled = model.predict(input_data, verbose=0)
    pred_price_raw = scaler.inverse_transform(pred_scaled)[0][0]
    
    change = pred_price_raw - last_price
    change = np.clip(change, -MAX_DAILY_CHANGE*last_price, MAX_DAILY_CHANGE*last_price)
    anchored_price = last_price + change
    predictions.append(anchored_price)
    
    pred_scaled_reshaped = scaler.transform(np.array([[anchored_price]]))
    current_seq = np.append(current_seq[1:], pred_scaled_reshaped, axis=0)
    last_price = anchored_price

prediction_dates = pd.date_range(start=data.index[-1] + pd.Timedelta(days=1), periods=7)
results_df = pd.DataFrame({'Date': prediction_dates, 'Predicted_Price': np.round(predictions, 2)})

print("\nLSTM - 7-Day Forecast")
print("="*50)
print(results_df.to_string(index=False))