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| import pandas as pd | |
| import gradio as gr | |
| import numpy as np | |
| import matplotlib.pyplot as plt | |
| from statsmodels.tsa.api import Holt, ExponentialSmoothing | |
| from statsmodels.tsa.arima.model import ARIMA | |
| def forecast_methods(train, h, methods): | |
| forecast = [] | |
| titles = [] | |
| if methods['naive']: | |
| naive_forecast = np.tile(train.iloc[-1], h) | |
| forecast.append(naive_forecast) | |
| titles.append("Naive") | |
| if methods['mean']: | |
| mean_forecast = np.tile(train.mean(), h) | |
| forecast.append(mean_forecast) | |
| titles.append("Mean") | |
| if methods['drift']: | |
| drift_forecast = train.iloc[-1] + (np.arange(1, h + 1) * | |
| ((train.iloc[-1] - train.iloc[0]) / (len(train) - 1))) | |
| forecast.append(drift_forecast) | |
| titles.append("Drift") | |
| if methods['holt']: | |
| holt_forecast = Holt(train).fit().forecast(h) | |
| forecast.append(holt_forecast) | |
| titles.append("Holt") | |
| if methods['hw']: | |
| hw_forecast = ExponentialSmoothing(train, seasonal='additive', | |
| seasonal_periods=12).fit().forecast(h) | |
| forecast.append(hw_forecast) | |
| titles.append("HW Additive") | |
| if methods['arima']: | |
| arima_model = ARIMA(train, order=(1, 1, 1)).fit() | |
| arima_forecast = arima_model.forecast(steps=h) | |
| forecast.append(arima_forecast) | |
| titles.append("ARIMA") | |
| return forecast, titles | |
| def forecast_benchmark(file, forecast_horizon, naive, mean, drift, holt, hw, arima): | |
| data = pd.read_csv(file.name, header=None) | |
| train = data.iloc[:, 0] | |
| methods = { | |
| 'naive': naive, | |
| 'mean': mean, | |
| 'drift': drift, | |
| 'holt': holt, | |
| 'hw': hw, | |
| 'arima': arima, | |
| } | |
| forecasts, titles = forecast_methods(train, forecast_horizon, methods) | |
| # Criar o gráfico | |
| plt.figure(figsize=(10, 6)) | |
| plt.plot(train, label="Dados Atuais") | |
| for forecast, title in zip(forecasts, titles): | |
| plt.plot(np.arange(len(train), len(train) + len(forecast)), forecast, label=title) | |
| plt.legend() | |
| plt.title("Benchmark de Séries Temporais") | |
| plt.grid(True) | |
| plt.savefig("forecast_plot.png") | |
| plt.close() | |
| return "forecast_plot.png" | |
| # Interface Gradio | |
| iface = gr.Interface( | |
| fn=forecast_benchmark, | |
| inputs=[ | |
| gr.File(label="Upload CSV file"), | |
| gr.Slider(minimum=1, maximum=60, step=1, value=24, label="Forecast Horizon"), | |
| gr.Checkbox(label="Naive"), | |
| gr.Checkbox(label="Mean"), | |
| gr.Checkbox(label="Drift"), | |
| gr.Checkbox(label="Holt"), | |
| gr.Checkbox(label="Holt-Winters (Additive)"), | |
| gr.Checkbox(label="ARIMA"), | |
| ], | |
| outputs=gr.Image(label="Forecast Plot"), | |
| title="Time Series Forecasting Benchmark", | |
| description="Upload a CSV com dados de série temporal (1 coluna). Escolha os métodos de previsão para comparar." | |
| ) | |
| iface.launch() | |