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| import pandas as pd | |
| import matplotlib.pyplot as plt | |
| import gradio as gr | |
| import tempfile | |
| import os | |
| from datetime import datetime | |
| from statsforecast import StatsForecast | |
| from statsforecast.models import ( | |
| HistoricAverage, | |
| Naive, | |
| SeasonalNaive, | |
| WindowAverage, | |
| SeasonalWindowAverage, | |
| AutoETS, | |
| AutoARIMA | |
| ) | |
| from utilsforecast.evaluation import evaluate | |
| from utilsforecast.losses import * | |
| # Import for TimeGPT | |
| from nixtla import NixtlaClient | |
| # Function to load and process uploaded CSV | |
| def load_data(file): | |
| if file is None: | |
| return None, "Please upload a CSV file" | |
| try: | |
| df = pd.read_csv(file) | |
| required_cols = ['unique_id', 'ds', 'y'] | |
| missing_cols = [col for col in required_cols if col not in df.columns] | |
| if missing_cols: | |
| return None, f"Missing required columns: {', '.join(missing_cols)}" | |
| df['ds'] = pd.to_datetime(df['ds']) | |
| df = df[required_cols] | |
| df = df.sort_values(['unique_id', 'ds']).reset_index(drop=True) | |
| # Check for NaN values | |
| if df['y'].isna().any(): | |
| return None, "Data contains missing values in the 'y' column" | |
| return df, "Data loaded successfully!" | |
| except Exception as e: | |
| return None, f"Error loading data: {str(e)}" | |
| # Function to generate and return a plot | |
| def create_forecast_plot(forecast_df, original_df, title="Forecasting Results"): | |
| plt.figure(figsize=(12, 7)) | |
| unique_ids = forecast_df['unique_id'].unique() | |
| forecast_cols = [col for col in forecast_df.columns if col not in ['unique_id', 'ds', 'cutoff']] | |
| colors = plt.cm.tab10.colors | |
| for i, unique_id in enumerate(unique_ids): | |
| original_data = original_df[original_df['unique_id'] == unique_id] | |
| plt.plot(original_data['ds'], original_data['y'], 'k-', linewidth=2, label=f'{unique_id} (Actual)') | |
| forecast_data = forecast_df[forecast_df['unique_id'] == unique_id] | |
| for j, col in enumerate(forecast_cols): | |
| if col in forecast_data.columns: | |
| plt.plot(forecast_data['ds'], forecast_data[col], | |
| color=colors[j % len(colors)], | |
| linestyle='--', | |
| linewidth=1.5, | |
| label=f'{col}') | |
| plt.title(title, fontsize=16) | |
| plt.xlabel('Date', fontsize=12) | |
| plt.ylabel('Value', fontsize=12) | |
| plt.grid(True, alpha=0.3) | |
| plt.legend(loc='best') | |
| plt.tight_layout() | |
| # Format date labels better | |
| fig = plt.gcf() | |
| ax = plt.gca() | |
| fig.autofmt_xdate() | |
| return fig | |
| # Function to create a plot for future forecasts | |
| def create_future_forecast_plot(forecast_df, original_df): | |
| plt.figure(figsize=(12, 7)) | |
| unique_ids = forecast_df['unique_id'].unique() | |
| forecast_cols = [col for col in forecast_df.columns if col not in ['unique_id', 'ds']] | |
| colors = plt.cm.tab10.colors | |
| for i, unique_id in enumerate(unique_ids): | |
| # Plot historical data | |
| original_data = original_df[original_df['unique_id'] == unique_id] | |
| plt.plot(original_data['ds'], original_data['y'], 'k-', linewidth=2, label=f'{unique_id} (Historical)') | |
| # Plot forecast data with shaded vertical line separator | |
| forecast_data = forecast_df[forecast_df['unique_id'] == unique_id] | |
| # Add vertical line at the forecast start | |
| if not forecast_data.empty and not original_data.empty: | |
| forecast_start = forecast_data['ds'].min() | |
| plt.axvline(x=forecast_start, color='gray', linestyle='--', alpha=0.5) | |
| for j, col in enumerate(forecast_cols): | |
| if col in forecast_data.columns: | |
| plt.plot(forecast_data['ds'], forecast_data[col], | |
| color=colors[j % len(colors)], | |
| linestyle='--', | |
| linewidth=1.5, | |
| label=f'{col}') | |
| plt.title('Future Forecast', fontsize=16) | |
| plt.xlabel('Date', fontsize=12) | |
| plt.ylabel('Value', fontsize=12) | |
| plt.grid(True, alpha=0.3) | |
| plt.legend(loc='best') | |
| plt.tight_layout() | |
| # Format date labels better | |
| fig = plt.gcf() | |
| ax = plt.gca() | |
| fig.autofmt_xdate() | |
| return fig | |
| # Function to export results to CSV | |
| def export_results(eval_df, cv_results, future_forecasts): | |
| timestamp = datetime.now().strftime("%Y%m%d_%H%M%S") | |
| # Create temp directory if it doesn't exist | |
| temp_dir = tempfile.mkdtemp() | |
| result_files = [] | |
| if eval_df is not None: | |
| eval_path = os.path.join(temp_dir, f"evaluation_metrics_{timestamp}.csv") | |
| eval_df.to_csv(eval_path, index=False) | |
| result_files.append(eval_path) | |
| if cv_results is not None: | |
| cv_path = os.path.join(temp_dir, f"cross_validation_results_{timestamp}.csv") | |
| cv_results.to_csv(cv_path, index=False) | |
| result_files.append(cv_path) | |
| if future_forecasts is not None: | |
| forecast_path = os.path.join(temp_dir, f"forecasts_{timestamp}.csv") | |
| future_forecasts.to_csv(forecast_path, index=False) | |
| result_files.append(forecast_path) | |
| return result_files | |
| # Main forecasting logic | |
| def run_forecast( | |
| file, | |
| frequency, | |
| eval_strategy, | |
| horizon, | |
| step_size, | |
| num_windows, | |
| use_historical_avg, | |
| use_naive, | |
| use_seasonal_naive, | |
| seasonality, | |
| use_window_avg, | |
| window_size, | |
| use_seasonal_window_avg, | |
| seasonal_window_size, | |
| use_autoets, | |
| use_autoarima, | |
| use_timegpt, | |
| finetune_loss, | |
| confidence_level, | |
| future_horizon | |
| ): | |
| df, message = load_data(file) | |
| if df is None: | |
| return None, None, None, None, None, None, message | |
| # Initialize results | |
| eval_df = None | |
| cv_results = None | |
| future_forecasts = None | |
| # Set up traditional statistical models | |
| models = [] | |
| model_aliases = [] | |
| if use_historical_avg: | |
| models.append(HistoricAverage(alias='historical_average')) | |
| model_aliases.append('historical_average') | |
| if use_naive: | |
| models.append(Naive(alias='naive')) | |
| model_aliases.append('naive') | |
| if use_seasonal_naive: | |
| models.append(SeasonalNaive(season_length=seasonality, alias='seasonal_naive')) | |
| model_aliases.append('seasonal_naive') | |
| if use_window_avg: | |
| models.append(WindowAverage(window_size=window_size, alias='window_average')) | |
| model_aliases.append('window_average') | |
| if use_seasonal_window_avg: | |
| models.append(SeasonalWindowAverage(season_length=seasonality, window_size=seasonal_window_size, alias='seasonal_window_average')) | |
| model_aliases.append('seasonal_window_average') | |
| if use_autoets: | |
| models.append(AutoETS(alias='autoets', season_length=seasonality)) | |
| model_aliases.append('autoets') | |
| if use_autoarima: | |
| models.append(AutoARIMA(alias='autoarima', season_length=seasonality)) | |
| model_aliases.append('autoarima') | |
| if not models and not use_timegpt: | |
| return None, None, None, None, None, None, "Please select at least one forecasting model" | |
| try: | |
| # Initialize results with empty DataFrames | |
| combined_eval_df = pd.DataFrame() | |
| combined_cv_results = pd.DataFrame() | |
| combined_future_forecasts = pd.DataFrame() | |
| # Run traditional statistical models if any are selected | |
| if models: | |
| sf = StatsForecast(models=models, freq=frequency, n_jobs=-1) | |
| # Run cross-validation for traditional models | |
| if eval_strategy == "Cross Validation": | |
| cv_results = sf.cross_validation(df=df, h=horizon, step_size=step_size, n_windows=num_windows) | |
| evaluation = evaluate(df=cv_results, metrics=[bias, mae, rmse, mape], models=model_aliases) | |
| eval_df = pd.DataFrame(evaluation).reset_index() | |
| else: # Fixed window | |
| cv_results = sf.cross_validation(df=df, h=horizon, step_size=10, n_windows=1) # any step size for 1 window | |
| evaluation = evaluate(df=cv_results, metrics=[bias, mae, rmse, mape], models=model_aliases) | |
| eval_df = pd.DataFrame(evaluation).reset_index() | |
| # Generate future forecasts | |
| future_forecasts = sf.forecast(df=df, h=future_horizon) | |
| # Store results | |
| combined_eval_df = eval_df.copy() if eval_df is not None else pd.DataFrame() | |
| combined_cv_results = cv_results.copy() if cv_results is not None else pd.DataFrame() | |
| combined_future_forecasts = future_forecasts.copy() if future_forecasts is not None else pd.DataFrame() | |
| # Run TimeGPT if selected | |
| if use_timegpt: | |
| try: | |
| # Get API key from environment variables | |
| nixtla_api_key = os.getenv("NIXTLA_API_KEY") | |
| if not nixtla_api_key: | |
| return None, None, None, None, None, None, "TimeGPT API key not found. Please set the NIXTLA_API_KEY environment variable." | |
| # Initialize Nixtla client | |
| nixtla_client = NixtlaClient(api_key=nixtla_api_key) | |
| # Convert confidence_level to list format | |
| level = [float(confidence_level)] | |
| # Run cross-validation for TimeGPT | |
| if eval_strategy == "Cross Validation": | |
| timegpt_cv_df = nixtla_client.cross_validation( | |
| df=df, | |
| h=horizon, | |
| freq=frequency, | |
| level=level, | |
| n_windows=num_windows, | |
| step_size=step_size | |
| ) | |
| timegpt_cv_eval = evaluate( | |
| df=timegpt_cv_df, | |
| metrics=[mape, mae, rmse, bias], | |
| models=['TimeGPT'], | |
| level=level | |
| ) | |
| timegpt_eval_df = pd.DataFrame(timegpt_cv_eval).reset_index() | |
| else: # Fixed window | |
| timegpt_cv_df = nixtla_client.cross_validation( | |
| df=df, | |
| h=horizon, | |
| freq=frequency, | |
| level=level, | |
| n_windows=1, | |
| step_size=10 | |
| ) | |
| timegpt_cv_eval = evaluate( | |
| df=timegpt_cv_df, | |
| metrics=[mape, mae, rmse, bias], | |
| models=['TimeGPT'], | |
| level=level | |
| ) | |
| timegpt_eval_df = pd.DataFrame(timegpt_cv_eval).reset_index() | |
| # Generate future forecasts with TimeGPT | |
| forecast_timegpt = nixtla_client.forecast( | |
| df=df, | |
| h=future_horizon, | |
| freq=frequency, | |
| level=level, | |
| finetune_loss=finetune_loss | |
| ) | |
| # Combine results | |
| if not combined_eval_df.empty and not timegpt_eval_df.empty: | |
| combined_eval_df = pd.concat([combined_eval_df, timegpt_eval_df], ignore_index=True) | |
| else: | |
| combined_eval_df = timegpt_eval_df if not timegpt_eval_df.empty else combined_eval_df | |
| if not combined_cv_results.empty and not timegpt_cv_df.empty: | |
| # Make sure we're not duplicating the 'y' column | |
| if 'y' in combined_cv_results.columns and 'y' in timegpt_cv_df.columns: | |
| timegpt_cv_df_no_y = timegpt_cv_df.drop(columns=['y']) | |
| combined_cv_results = pd.merge( | |
| combined_cv_results, | |
| timegpt_cv_df_no_y, | |
| on=['unique_id', 'ds', 'cutoff'], | |
| how='outer' | |
| ) | |
| else: | |
| combined_cv_results = pd.concat([combined_cv_results, timegpt_cv_df], ignore_index=True) | |
| else: | |
| combined_cv_results = timegpt_cv_df if not timegpt_cv_df.empty else combined_cv_results | |
| if not combined_future_forecasts.empty and not forecast_timegpt.empty: | |
| # Make sure we're merging on common columns | |
| combined_future_forecasts = pd.merge( | |
| combined_future_forecasts, | |
| forecast_timegpt, | |
| on=['unique_id', 'ds'], | |
| how='outer' | |
| ) | |
| else: | |
| combined_future_forecasts = forecast_timegpt if not forecast_timegpt.empty else combined_future_forecasts | |
| except Exception as e: | |
| return None, None, None, None, None, None, f"Error with TimeGPT: {str(e)}" | |
| # Create plots | |
| if not combined_cv_results.empty: | |
| fig_validation = create_forecast_plot( | |
| combined_cv_results, | |
| df, | |
| f"{eval_strategy} Results" | |
| ) | |
| else: | |
| fig_validation = None | |
| if not combined_future_forecasts.empty: | |
| fig_future = create_future_forecast_plot(combined_future_forecasts, df) | |
| else: | |
| fig_future = None | |
| # Export results | |
| export_files = export_results(combined_eval_df, combined_cv_results, combined_future_forecasts) | |
| return combined_eval_df, combined_cv_results, fig_validation, combined_future_forecasts, fig_future, export_files, "Analysis completed successfully!" | |
| except Exception as e: | |
| return None, None, None, None, None, None, f"Error during forecasting: {str(e)}" | |
| # Sample CSV file generation | |
| def download_sample(): | |
| sample_data = """unique_id,ds,y | |
| ^GSPC,2023-01-03,3824.139892578125 | |
| ^GSPC,2023-01-04,3852.969970703125 | |
| ^GSPC,2023-01-05,3808.10009765625 | |
| ^GSPC,2023-01-06,3895.080078125 | |
| ^GSPC,2023-01-09,3892.090087890625 | |
| ^GSPC,2023-01-10,3919.25 | |
| ^GSPC,2023-01-11,3969.610107421875 | |
| ^GSPC,2023-01-12,3983.169921875 | |
| ^GSPC,2023-01-13,3999.090087890625 | |
| ^GSPC,2023-01-17,3990.969970703125 | |
| ^GSPC,2023-01-18,3928.860107421875 | |
| ^GSPC,2023-01-19,3898.85009765625 | |
| ^GSPC,2023-01-20,3972.610107421875 | |
| ^GSPC,2023-01-23,4019.81005859375 | |
| ^GSPC,2023-01-24,4016.949951171875 | |
| ^GSPC,2023-01-25,4016.219970703125 | |
| ^GSPC,2023-01-26,4060.429931640625 | |
| ^GSPC,2023-01-27,4070.56005859375 | |
| ^GSPC,2023-01-30,4017.77001953125 | |
| ^GSPC,2023-01-31,4076.60009765625 | |
| ^GSPC,2023-02-01,4119.2099609375 | |
| ^GSPC,2023-02-02,4179.759765625 | |
| ^GSPC,2023-02-03,4136.47998046875 | |
| ^GSPC,2023-02-06,4111.080078125 | |
| ^GSPC,2023-02-07,4164 | |
| ^GSPC,2023-02-08,4117.85986328125 | |
| ^GSPC,2023-02-09,4081.5 | |
| ^GSPC,2023-02-10,4090.4599609375 | |
| ^GSPC,2023-02-13,4137.2900390625 | |
| ^GSPC,2023-02-14,4136.1298828125 | |
| ^GSPC,2023-02-15,4147.60009765625 | |
| ^GSPC,2023-02-16,4090.409912109375 | |
| ^GSPC,2023-02-17,4079.090087890625 | |
| ^GSPC,2023-02-21,3997.340087890625 | |
| ^GSPC,2023-02-22,3991.050048828125 | |
| ^GSPC,2023-02-23,4012.320068359375 | |
| ^GSPC,2023-02-24,3970.0400390625 | |
| ^GSPC,2023-02-27,3982.239990234375 | |
| ^GSPC,2023-02-28,3970.14990234375 | |
| ^GSPC,2023-03-01,3951.389892578125 | |
| ^GSPC,2023-03-02,3981.35009765625 | |
| ^GSPC,2023-03-03,4045.639892578125 | |
| ^GSPC,2023-03-06,4048.419921875 | |
| ^GSPC,2023-03-07,3986.3701171875 | |
| ^GSPC,2023-03-08,3992.010009765625 | |
| ^GSPC,2023-03-09,3918.320068359375 | |
| ^GSPC,2023-03-10,3861.590087890625 | |
| ^GSPC,2023-03-13,3855.760009765625 | |
| ^GSPC,2023-03-14,3919.2900390625 | |
| ^GSPC,2023-03-15,3891.929931640625 | |
| ^GSPC,2023-03-16,3960.280029296875 | |
| ^GSPC,2023-03-17,3916.639892578125 | |
| ^GSPC,2023-03-20,3951.570068359375 | |
| ^GSPC,2023-03-21,4002.8701171875 | |
| ^GSPC,2023-03-22,3936.969970703125 | |
| ^GSPC,2023-03-23,3948.719970703125 | |
| ^GSPC,2023-03-24,3970.989990234375 | |
| ^GSPC,2023-03-27,3977.530029296875 | |
| ^GSPC,2023-03-28,3971.27001953125 | |
| ^GSPC,2023-03-29,4027.81005859375 | |
| ^GSPC,2023-03-30,4050.830078125 | |
| ^GSPC,2023-03-31,4109.31005859375 | |
| ^GSPC,2023-04-03,4124.509765625 | |
| ^GSPC,2023-04-04,4100.60009765625 | |
| ^GSPC,2023-04-05,4090.3798828125 | |
| ^GSPC,2023-04-06,4105.02001953125 | |
| ^GSPC,2023-04-10,4109.10986328125 | |
| ^GSPC,2023-04-11,4108.93994140625 | |
| ^GSPC,2023-04-12,4091.949951171875 | |
| ^GSPC,2023-04-13,4146.22021484375 | |
| ^GSPC,2023-04-14,4137.64013671875 | |
| ^GSPC,2023-04-17,4151.31982421875 | |
| ^GSPC,2023-04-18,4154.8701171875 | |
| ^GSPC,2023-04-19,4154.52001953125 | |
| ^GSPC,2023-04-20,4129.7900390625 | |
| ^GSPC,2023-04-21,4133.52001953125 | |
| ^GSPC,2023-04-24,4137.0400390625 | |
| ^GSPC,2023-04-25,4071.6298828125 | |
| ^GSPC,2023-04-26,4055.989990234375 | |
| ^GSPC,2023-04-27,4135.35009765625 | |
| ^GSPC,2023-04-28,4169.47998046875 | |
| ^GSPC,2023-05-01,4167.8701171875 | |
| ^GSPC,2023-05-02,4119.580078125 | |
| ^GSPC,2023-05-03,4090.75 | |
| ^GSPC,2023-05-04,4061.219970703125 | |
| ^GSPC,2023-05-05,4136.25 | |
| ^GSPC,2023-05-08,4138.1201171875 | |
| ^GSPC,2023-05-09,4119.169921875 | |
| ^GSPC,2023-05-10,4137.64013671875 | |
| ^GSPC,2023-05-11,4130.6201171875 | |
| ^GSPC,2023-05-12,4124.080078125 | |
| ^GSPC,2023-05-15,4136.27978515625 | |
| ^GSPC,2023-05-16,4109.89990234375 | |
| ^GSPC,2023-05-17,4158.77001953125 | |
| ^GSPC,2023-05-18,4198.0498046875 | |
| ^GSPC,2023-05-19,4191.97998046875 | |
| ^GSPC,2023-05-22,4192.6298828125 | |
| ^GSPC,2023-05-23,4145.580078125 | |
| ^GSPC,2023-05-24,4115.240234375 | |
| ^GSPC,2023-05-25,4151.27978515625 | |
| ^GSPC,2023-05-26,4205.4501953125 | |
| ^GSPC,2023-05-30,4205.52001953125 | |
| ^GSPC,2023-05-31,4179.830078125 | |
| ^GSPC,2023-06-01,4221.02001953125 | |
| ^GSPC,2023-06-02,4282.3701171875 | |
| ^GSPC,2023-06-05,4273.7900390625 | |
| ^GSPC,2023-06-06,4283.85009765625 | |
| ^GSPC,2023-06-07,4267.52001953125 | |
| ^GSPC,2023-06-08,4293.93017578125 | |
| ^GSPC,2023-06-09,4298.85986328125 | |
| ^GSPC,2023-06-12,4338.93017578125 | |
| ^GSPC,2023-06-13,4369.009765625 | |
| ^GSPC,2023-06-14,4372.58984375 | |
| ^GSPC,2023-06-15,4425.83984375 | |
| ^GSPC,2023-06-16,4409.58984375 | |
| ^GSPC,2023-06-20,4388.7099609375 | |
| ^GSPC,2023-06-21,4365.68994140625 | |
| ^GSPC,2023-06-22,4381.89013671875 | |
| ^GSPC,2023-06-23,4348.330078125 | |
| ^GSPC,2023-06-26,4328.81982421875 | |
| ^GSPC,2023-06-27,4378.41015625 | |
| ^GSPC,2023-06-28,4376.85986328125 | |
| ^GSPC,2023-06-29,4396.43994140625 | |
| ^GSPC,2023-06-30,4450.3798828125 | |
| ^GSPC,2023-07-03,4455.58984375 | |
| ^GSPC,2023-07-05,4446.81982421875 | |
| ^GSPC,2023-07-06,4411.58984375 | |
| ^GSPC,2023-07-07,4398.9501953125 | |
| ^GSPC,2023-07-10,4409.52978515625 | |
| ^GSPC,2023-07-11,4439.259765625 | |
| ^GSPC,2023-07-12,4472.16015625 | |
| ^GSPC,2023-07-13,4510.0400390625 | |
| ^GSPC,2023-07-14,4505.419921875 | |
| ^GSPC,2023-07-17,4522.7900390625 | |
| ^GSPC,2023-07-18,4554.97998046875 | |
| ^GSPC,2023-07-19,4565.72021484375 | |
| ^GSPC,2023-07-20,4534.8701171875 | |
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| ^GSPC,2024-11-18,5893.6201171875 | |
| ^GSPC,2024-11-19,5916.97998046875 | |
| ^GSPC,2024-11-20,5917.10986328125 | |
| ^GSPC,2024-11-21,5948.7099609375 | |
| ^GSPC,2024-11-22,5969.33984375 | |
| ^GSPC,2024-11-25,5987.3701171875 | |
| ^GSPC,2024-11-26,6021.6298828125 | |
| ^GSPC,2024-11-27,5998.740234375 | |
| ^GSPC,2024-11-29,6032.3798828125 | |
| ^GSPC,2024-12-02,6047.14990234375 | |
| ^GSPC,2024-12-03,6049.8798828125 | |
| ^GSPC,2024-12-04,6086.490234375 | |
| ^GSPC,2024-12-05,6075.10986328125 | |
| ^GSPC,2024-12-06,6090.27001953125 | |
| ^GSPC,2024-12-09,6052.85009765625 | |
| ^GSPC,2024-12-10,6034.91015625 | |
| ^GSPC,2024-12-11,6084.18994140625 | |
| ^GSPC,2024-12-12,6051.25 | |
| ^GSPC,2024-12-13,6051.08984375 | |
| ^GSPC,2024-12-16,6074.080078125 | |
| ^GSPC,2024-12-17,6050.60986328125 | |
| ^GSPC,2024-12-18,5872.16015625 | |
| ^GSPC,2024-12-19,5867.080078125 | |
| ^GSPC,2024-12-20,5930.85009765625 | |
| ^GSPC,2024-12-23,5974.06982421875 | |
| ^GSPC,2024-12-24,6040.0400390625 | |
| ^GSPC,2024-12-26,6037.58984375 | |
| ^GSPC,2024-12-27,5970.83984375 | |
| ^GSPC,2024-12-30,5906.93994140625 | |
| ^GSPC,2024-12-31,5881.6298828125 | |
| ^GSPC,2025-01-02,5868.5498046875 | |
| ^GSPC,2025-01-03,5942.47021484375 | |
| ^GSPC,2025-01-06,5975.3798828125 | |
| ^GSPC,2025-01-07,5909.02978515625 | |
| ^GSPC,2025-01-08,5918.25 | |
| ^GSPC,2025-01-10,5827.0400390625 | |
| ^GSPC,2025-01-13,5836.22021484375 | |
| ^GSPC,2025-01-14,5842.91015625 | |
| ^GSPC,2025-01-15,5949.91015625 | |
| ^GSPC,2025-01-16,5937.33984375 | |
| ^GSPC,2025-01-17,5996.66015625 | |
| ^GSPC,2025-01-21,6049.240234375 | |
| ^GSPC,2025-01-22,6086.3701171875 | |
| ^GSPC,2025-01-23,6118.7099609375 | |
| ^GSPC,2025-01-24,6101.240234375 | |
| ^GSPC,2025-01-27,6012.27978515625 | |
| ^GSPC,2025-01-28,6067.7001953125 | |
| ^GSPC,2025-01-29,6039.31005859375 | |
| ^GSPC,2025-01-30,6071.169921875 | |
| ^GSPC,2025-01-31,6040.52978515625 | |
| ^GSPC,2025-02-03,5994.56982421875 | |
| ^GSPC,2025-02-04,6037.8798828125 | |
| ^GSPC,2025-02-05,6061.47998046875 | |
| ^GSPC,2025-02-06,6083.56982421875 | |
| ^GSPC,2025-02-07,6025.990234375 | |
| ^GSPC,2025-02-10,6066.43994140625 | |
| ^GSPC,2025-02-11,6068.5 | |
| ^GSPC,2025-02-12,6051.97021484375 | |
| ^GSPC,2025-02-13,6115.06982421875 | |
| ^GSPC,2025-02-14,6114.6298828125 | |
| ^GSPC,2025-02-18,6129.580078125 | |
| ^GSPC,2025-02-19,6144.14990234375 | |
| ^GSPC,2025-02-20,6117.52001953125 | |
| ^GSPC,2025-02-21,6013.1298828125 | |
| ^GSPC,2025-02-24,5983.25 | |
| ^GSPC,2025-02-25,5955.25 | |
| ^GSPC,2025-02-26,5956.06005859375 | |
| ^GSPC,2025-02-27,5861.56982421875 | |
| ^GSPC,2025-02-28,5954.5 | |
| ^GSPC,2025-03-03,5849.72021484375 | |
| ^GSPC,2025-03-04,5778.14990234375 | |
| ^GSPC,2025-03-05,5842.6298828125 | |
| ^GSPC,2025-03-06,5738.52001953125 | |
| ^GSPC,2025-03-07,5770.2001953125 | |
| ^GSPC,2025-03-10,5614.56005859375 | |
| ^GSPC,2025-03-11,5572.06982421875 | |
| ^GSPC,2025-03-12,5599.2998046875 | |
| ^GSPC,2025-03-13,5521.52001953125 | |
| ^GSPC,2025-03-14,5638.93994140625 | |
| ^GSPC,2025-03-17,5675.1201171875 | |
| ^GSPC,2025-03-18,5614.66015625 | |
| ^GSPC,2025-03-19,5675.2900390625 | |
| ^GSPC,2025-03-20,5662.89013671875 | |
| ^GSPC,2025-03-21,5667.56005859375 | |
| ^GSPC,2025-03-24,5767.56982421875 | |
| ^GSPC,2025-03-25,5776.64990234375 | |
| ^GSPC,2025-03-26,5712.2001953125 | |
| ^GSPC,2025-03-27,5693.31005859375 | |
| ^GSPC,2025-03-28,5580.93994140625 | |
| ^GSPC,2025-03-31,5611.85009765625 | |
| """ | |
| temp = tempfile.NamedTemporaryFile(delete=False, suffix=".csv", mode='w', newline='') | |
| temp.write(sample_data) | |
| temp.close() | |
| return temp.name | |
| # Global theme | |
| theme = gr.themes.Soft( | |
| primary_hue="blue", | |
| secondary_hue="indigo", | |
| neutral_hue="gray" | |
| ) | |
| # Gradio interface | |
| with gr.Blocks(title="Time Series Forecasting App", theme=theme) as app: | |
| gr.Markdown("# 📈 Time Series Forecasting App") | |
| gr.Markdown("Upload a CSV with `unique_id`, `ds`, and `y` columns to apply forecasting models.") | |
| # Disclaimer about external predictors | |
| with gr.Accordion("Disclaimer", open=True): | |
| gr.Markdown(""" | |
| **Disclaimer:** For simplicity, this app does not allow the use of external predictors. | |
| However, they can be easily included in the underlying statsforecast (for AutoARIMA) | |
| and the TimeGPT implementation by Nixtla. To use external predictors, you would need to modify | |
| the code to include them in your forecasting models. | |
| """) | |
| with gr.Row(): | |
| with gr.Column(scale=2): | |
| file_input = gr.File(label="Upload CSV file", file_types=[".csv"]) | |
| download_btn = gr.Button("Download Sample Data", variant="secondary") | |
| download_output = gr.File(label="Click to download", visible=True) | |
| download_btn.click(fn=download_sample, outputs=download_output) | |
| with gr.Accordion("Data & Validation Settings", open=True): | |
| frequency = gr.Dropdown( | |
| choices=[ | |
| ("Hourly", "H"), | |
| ("Daily", "D"), | |
| ("Weekly", "WS"), | |
| ("Monthly", "MS"), | |
| ("Quarterly", "QS"), | |
| ("Yearly", "YS") | |
| ], | |
| label="Data Frequency", | |
| value="D" | |
| ) | |
| # Evaluation Strategy | |
| eval_strategy = gr.Radio( | |
| choices=["Fixed Window", "Cross Validation"], | |
| label="Evaluation Strategy", | |
| value="Cross Validation" | |
| ) | |
| # Fixed Window settings | |
| with gr.Group(visible=True) as fixed_window_box: | |
| gr.Markdown("### Fixed Window Settings") | |
| horizon = gr.Slider(1, 100, value=10, step=1, label="Validation Horizon (steps ahead to predict)") | |
| # Cross Validation settings | |
| with gr.Group(visible=True) as cv_box: | |
| gr.Markdown("### Cross Validation Settings") | |
| with gr.Row(): | |
| step_size = gr.Slider(1, 50, value=10, step=1, label="Step Size (distance between windows)") | |
| num_windows = gr.Slider(1, 20, value=5, step=1, label="Number of Windows") | |
| # Future forecast settings (always visible) | |
| with gr.Group(): | |
| gr.Markdown("### Future Forecast Settings") | |
| future_horizon = gr.Slider(1, 100, value=10, step=1, label="Future Forecast Horizon (steps to predict)") | |
| with gr.Accordion("Model Configuration", open=True): | |
| with gr.Tabs() as model_tabs: | |
| # Traditional Statistical Models Tab | |
| with gr.TabItem("Statistical Models"): | |
| gr.Markdown("## Basic Models") | |
| with gr.Row(): | |
| use_historical_avg = gr.Checkbox(label="Historical Average", value=True) | |
| use_naive = gr.Checkbox(label="Naive", value=True) | |
| # Common seasonality parameter at the top level | |
| with gr.Group(): | |
| gr.Markdown("### Seasonality Configuration") | |
| gr.Markdown("This seasonality period affects Seasonal Naive, Seasonal Window Average, AutoETS, and AutoARIMA models") | |
| seasonality = gr.Number(label="Seasonality Period", value=5) | |
| gr.Markdown("### Seasonal Models") | |
| with gr.Row(): | |
| use_seasonal_naive = gr.Checkbox(label="Seasonal Naive", value=True) | |
| gr.Markdown("### Window-based Models") | |
| with gr.Row(): | |
| use_window_avg = gr.Checkbox(label="Window Average", value=True) | |
| window_size = gr.Number(label="Window Size", value=10) | |
| with gr.Row(): | |
| use_seasonal_window_avg = gr.Checkbox(label="Seasonal Window Average", value=True) | |
| seasonal_window_size = gr.Number(label="Seasonal Window Size", value=2) | |
| gr.Markdown("### Advanced Models (use seasonality from above)") | |
| with gr.Row(): | |
| use_autoets = gr.Checkbox(label="AutoETS (Exponential Smoothing)", value=True) | |
| use_autoarima = gr.Checkbox(label="AutoARIMA", value=True) | |
| # Transformer Models Tab (TimeGPT) | |
| with gr.TabItem("Transformer Models"): | |
| gr.Markdown("## TimeGPT Model") | |
| gr.Markdown("TimeGPT uses a transformer architecture for state-of-the-art time series forecasting") | |
| with gr.Row(): | |
| use_timegpt = gr.Checkbox(label="Use TimeGPT", value=False) | |
| with gr.Group(): | |
| gr.Markdown("### TimeGPT Configuration") | |
| with gr.Row(): | |
| finetune_loss = gr.Dropdown( | |
| choices=["mape", "mae", "rmse", "smape"], | |
| label="Finetune Loss Metric", | |
| value="mape" | |
| ) | |
| confidence_level = gr.Slider(50, 99, value=95, step=1, label="Confidence Level (%)") | |
| gr.Markdown(""" | |
| **Note:** Using TimeGPT requires a valid API key. The API key should | |
| be set as an environment variable named `NIXTLA_API_KEY`. This space uses a trial key, which is rate limited. | |
| """) | |
| with gr.Column(scale=3): | |
| message_output = gr.Textbox(label="Status Message") | |
| with gr.Tabs() as tabs: | |
| with gr.TabItem("Validation Results"): | |
| eval_output = gr.Dataframe(label="Evaluation Metrics") | |
| validation_plot = gr.Plot(label="Validation Plot") | |
| validation_output = gr.Dataframe(label="Validation Data", visible=False) | |
| with gr.Row(): | |
| show_data_btn = gr.Button("Show Validation Data") | |
| hide_data_btn = gr.Button("Hide Validation Data", visible=False) | |
| def show_data(): | |
| return gr.update(visible=True), gr.update(visible=True), gr.update(visible=False) | |
| def hide_data(): | |
| return gr.update(visible=False), gr.update(visible=False), gr.update(visible=True) | |
| show_data_btn.click( | |
| fn=show_data, | |
| outputs=[validation_output, hide_data_btn, show_data_btn] | |
| ) | |
| hide_data_btn.click( | |
| fn=hide_data, | |
| outputs=[validation_output, hide_data_btn, show_data_btn] | |
| ) | |
| with gr.TabItem("Future Forecast"): | |
| forecast_plot = gr.Plot(label="Future Forecast Plot") | |
| forecast_output = gr.Dataframe(label="Future Forecast Data", visible=False) | |
| with gr.Row(): | |
| show_forecast_btn = gr.Button("Show Forecast Data") | |
| hide_forecast_btn = gr.Button("Hide Forecast Data", visible=False) | |
| def show_forecast(): | |
| return gr.update(visible=True), gr.update(visible=True), gr.update(visible=False) | |
| def hide_forecast(): | |
| return gr.update(visible=False), gr.update(visible=False), gr.update(visible=True) | |
| show_forecast_btn.click( | |
| fn=show_forecast, | |
| outputs=[forecast_output, hide_forecast_btn, show_forecast_btn] | |
| ) | |
| hide_forecast_btn.click( | |
| fn=hide_forecast, | |
| outputs=[forecast_output, hide_forecast_btn, show_forecast_btn] | |
| ) | |
| with gr.TabItem("Export Results"): | |
| export_files = gr.Files(label="Download Results") | |
| with gr.Row(visible=True) as run_row: | |
| submit_btn = gr.Button("Run Validation and Forecast", variant="primary", size="lg") | |
| # Update visibility of the appropriate box based on evaluation strategy | |
| def update_eval_boxes(strategy): | |
| return (gr.update(visible=strategy == "Fixed Window"), | |
| gr.update(visible=strategy == "Cross Validation")) | |
| eval_strategy.change( | |
| fn=update_eval_boxes, | |
| inputs=[eval_strategy], | |
| outputs=[fixed_window_box, cv_box] | |
| ) | |
| # Run forecast when button is clicked | |
| submit_btn.click( | |
| fn=run_forecast, | |
| inputs=[ | |
| file_input, frequency, eval_strategy, horizon, step_size, num_windows, | |
| use_historical_avg, use_naive, use_seasonal_naive, seasonality, | |
| use_window_avg, window_size, use_seasonal_window_avg, seasonal_window_size, | |
| use_autoets, use_autoarima, use_timegpt, finetune_loss, confidence_level, | |
| future_horizon | |
| ], | |
| outputs=[eval_output, validation_output, validation_plot, forecast_output, forecast_plot, export_files, message_output] | |
| ) | |
| if __name__ == "__main__": | |
| app.launch(share=False) |