import numpy as np import pandas as pd import itertools import plotly.graph_objects as go from statsmodels.tsa.statespace.sarimax import SARIMAX from sklearn.metrics import mean_absolute_error import gradio as gr from huggingface_hub import HfApi from skops import hub_utils from skops.card import Card, ModelIndex, DatasetCardData from tempfile import mkdtemp from pathlib import Path import pickle import pandas as pd import warnings warnings.filterwarnings("ignore") # Set random seed for reproducibility np.random.seed(42) # Load the dataset df = pd.read_csv("data/Top_12_German_Companies_Financial_Data.csv") companies = np.unique(df.Company) company = companies[9] print(f"Company: {company}") # Filter for the selected company df = df[df["Company"] == company].copy() df["Period"] = pd.to_datetime(df["Period"], format="%m/%d/%Y") df = df.sort_values(by="Period") df.set_index("Period", inplace=True) df["Revenue"] = pd.to_numeric(df["Revenue"], errors="coerce") series = df['Revenue'] # Train-validation-test split train_idx = int(len(series) * 0.8) val_idx = int(len(series) * 0.9) train, val, test = series[:train_idx], series[train_idx:val_idx], series[val_idx:] # Define parameter ranges for SARIMA tuning p_values, d_values, q_values = range(0, 6), range(0, 3), range(0, 6) P_values, D_values, Q_values = range(0, 3), range(0, 2), range(0, 3) S = 12 # Quarterly seasonality best_score, best_cfg = float("inf"), None # Grid search over SARIMA parameter combinations for p, d, q, P, D, Q in itertools.product(p_values, d_values, q_values, P_values, D_values, Q_values): try: model = SARIMAX(train, order=(p, d, q), seasonal_order=(P, D, Q, S), enforce_stationarity=False, enforce_invertibility=False) model_fit = model.fit(disp=False) predictions = model_fit.forecast(steps=len(val)) error = mean_absolute_error(val, predictions) if error < best_score: best_score, best_cfg = error, (p, d, q, P, D, Q) except: continue # Train best SARIMA model best_p, best_d, best_q, best_P, best_D, best_Q = best_cfg final_model = SARIMAX(pd.concat([train, val]), order=(best_p, best_d, best_q), seasonal_order=(best_P, best_D, best_Q, S), enforce_stationarity=False, enforce_invertibility=False, initialization="approximate_diffuse") final_model_fit = final_model.fit(disp=False) # Train on full dataset for next year prediction full_model = SARIMAX(series, order=(best_p, best_d, best_q), seasonal_order=(best_P, best_D, best_Q, S), enforce_stationarity=False, enforce_invertibility=False, initialization="approximate_diffuse") full_model_fit = full_model.fit(disp=False) def forecast_turnover(horizon, confidence_level): try: horizon = int(horizon) alpha_value = 1 - (confidence_level / 100) # Convert % to alpha predictions_result = final_model_fit.get_forecast(steps=horizon) final_predictions = predictions_result.predicted_mean conf_int = predictions_result.conf_int(alpha=alpha_value) last_date = test.index.min() future_dates = pd.date_range(start=last_date, periods=horizon, freq="Q") # Create interactive Plotly plot fig1 = go.Figure() fig1.add_trace(go.Scatter(x=train.index, y=train, mode='lines', name='Training Data', line=dict(color='blue'))) fig1.add_trace(go.Scatter(x=val.index, y=val, mode='lines', name='Validation Data', line=dict(color='orange'))) fig1.add_trace(go.Scatter(x=test.index, y=test, mode='lines+markers', name='Test Data', line=dict(color='green'))) fig1.add_trace(go.Scatter(x=future_dates, y=final_predictions, mode='lines+markers', name=f'Forecast ({confidence_level}%)', line=dict(color='red', dash='dash'))) # Confidence interval fill fig1.add_trace(go.Scatter( x=future_dates.tolist() + future_dates[::-1].tolist(), y=conf_int.iloc[:, 0].tolist() + conf_int.iloc[:, 1].tolist()[::-1], fill='toself', fillcolor='rgba(255, 0, 0, 0.2)', line=dict(color='rgba(255,255,255,0)'), showlegend=True, name=f'Confidence Interval ({confidence_level}%)' )) fig1.update_layout(title=f"SARIMA Forecast for {company} Revenue", xaxis_title="Year", yaxis_title="Revenue", hovermode='x') # Predict next year using full model next_year_result = full_model_fit.get_forecast(steps=4) next_year_predictions = next_year_result.predicted_mean next_year_conf_int = next_year_result.conf_int(alpha=alpha_value) next_year_dates = pd.date_range(start=series.index.max(), periods=4, freq="Q") fig2 = go.Figure() fig2.add_trace(go.Scatter(x=series.index, y=series, mode='lines', name='Full Data', line=dict(color='blue'))) fig2.add_trace(go.Scatter(x=next_year_dates, y=next_year_predictions, mode='lines+markers', name='Next Year Forecast', line=dict(color='red', dash='dash'))) fig2.add_trace(go.Scatter( x=next_year_dates.tolist() + next_year_dates[::-1].tolist(), y=next_year_conf_int.iloc[:, 0].tolist() + next_year_conf_int.iloc[:, 1].tolist()[::-1], fill='toself', fillcolor='rgba(255, 0, 0, 0.2)', line=dict(color='rgba(255,255,255,0)'), showlegend=True, name=f'Confidence Interval ({confidence_level}%)' )) fig2.update_layout(title=f"SARIMA Forecast for {company} Revenue for 2025", xaxis_title="Year", yaxis_title="Revenue", hovermode='x') return fig1, fig2 except Exception as e: return None, f"❌ Error: {str(e)}" # # Launch Gradio Interface # iface = gr.Interface( # fn=forecast_turnover, # inputs=[ # gr.Slider(minimum=1, maximum=6, step=1, label="Forecast Horizon (Quarters)"), # gr.Slider(minimum=50, maximum=99, step=1, label="Confidence Level (%)") # ], # outputs=[gr.Plot(), gr.Plot()], # title=f"{company} Revenue Forecast", # description="Select the forecast horizon (in quarters) and confidence level for revenue predictions.", # ) # iface.launch(debug=True) with gr.Blocks() as demo: gr.Markdown(f"# {company} Revenue Forecast") gr.Markdown("📈 Select the forecast horizon (in quarters) and confidence level for revenue predictions.") with gr.Column(): horizon = gr.Slider(minimum=1, maximum=6, step=1, label="Forecast Horizon (Quarters)") confidence = gr.Slider(minimum=50, maximum=99, step=1, label="Confidence Level (%)") submit = gr.Button("📊 Forecast") plot1 = gr.Plot(label="Validation & Forecast") plot2 = gr.Plot(label="Full Data & 2025 Forecast") def wrapped_forecast(h, c): return forecast_turnover(h, c) submit.click(fn=wrapped_forecast, inputs=[horizon, confidence], outputs=[plot1, plot2]) demo.launch(debug=True)