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models/time_series.py
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176
models/time_series.py
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import pandas as pd
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from statsmodels.tsa.seasonal import seasonal_decompose
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from statsmodels.tsa.holtwinters import ExponentialSmoothing
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import pmdarima as pm
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from prophet import Prophet
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import plotly.express as px
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import plotly.graph_objects as go
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from plotly.subplots import make_subplots
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import plotly.io as pio
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import numpy as np
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from sklearn.metrics import mean_absolute_error, mean_squared_error
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from utils.file_handling import save_processed_file
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from .plotting import create_acf_pacf_plots
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def process_time_series(filepath, do_decomposition, do_forecasting, do_acf_pacf, train_percent, forecast_periods,
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model_type):
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try:
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# Read file
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if filepath.endswith('.csv'):
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df = pd.read_csv(filepath)
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else:
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df = pd.read_excel(filepath)
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# Ensure datetime column exists
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date_col = df.columns[0] # Assume first column is date
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value_col = df.columns[1] # Assume second column is value
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df[date_col] = pd.to_datetime(df[date_col])
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df.set_index(date_col, inplace=True)
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# Initialize variables
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plot_html = None
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forecast_html = None
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acf_pacf_html = None
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summary = df[value_col].describe().to_dict()
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model_params = None
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train_size = None
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test_size = None
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metrics = None
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# Save processed data
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processed_df = df.copy()
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# Time series decomposition
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if do_decomposition:
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decomposition = seasonal_decompose(df[value_col], model='additive', period=12)
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fig = make_subplots(rows=4, cols=1,
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subplot_titles=('Original Series', 'Trend', 'Seasonality', 'Residuals'))
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fig.add_trace(go.Scatter(x=df.index, y=df[value_col], name='Original'), row=1, col=1)
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fig.add_trace(go.Scatter(x=df.index, y=decomposition.trend, name='Trend'), row=2, col=1)
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fig.add_trace(go.Scatter(x=df.index, y=decomposition.seasonal, name='Seasonality'), row=3, col=1)
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fig.add_trace(go.Scatter(x=df.index, y=decomposition.resid, name='Residuals'), row=4, col=1)
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fig.update_layout(height=800, showlegend=True)
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plot_html = pio.to_html(fig, full_html=False)
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processed_df['Trend'] = decomposition.trend
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processed_df['Seasonality'] = decomposition.seasonal
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processed_df['Residuals'] = decomposition.resid
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# Forecasting
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if do_forecasting:
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# Split data into train and test
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train_size = int(len(df) * train_percent)
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test_size = len(df) - train_size
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train_data = df[value_col].iloc[:train_size]
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test_data = df[value_col].iloc[train_size:] if test_size > 0 else pd.Series()
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forecast_dates = pd.date_range(start=df.index[-1], periods=forecast_periods + 1,
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freq=df.index.inferred_freq)[1:]
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# Initialize forecast and model parameters
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forecast = None
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if model_type == 'ARIMA':
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# Auto ARIMA for best parameters
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model = pm.auto_arima(train_data,
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seasonal=True,
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m=12,
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start_p=0, start_q=0,
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max_p=3, max_q=3,
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start_P=0, start_Q=0,
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max_P=2, max_Q=2,
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d=1, D=1,
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trace=False,
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error_action='ignore',
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suppress_warnings=True,
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stepwise=True)
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# Fit ARIMA with best parameters
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model_fit = model.fit(train_data)
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forecast = model_fit.predict(n_periods=forecast_periods)
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model_params = f"{model.order}, Seasonal{model.seasonal_order}"
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# Calculate metrics on test data if available
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if test_size > 0:
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test_predictions = model_fit.predict(n_periods=test_size)
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mae = mean_absolute_error(test_data, test_predictions)
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mse = mean_squared_error(test_data, test_predictions)
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rmse = np.sqrt(mse)
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metrics = {'MAE': mae, 'MSE': mse, 'RMSE': rmse}
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elif model_type == 'Exponential Smoothing':
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# Fit Exponential Smoothing model
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model = ExponentialSmoothing(train_data,
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trend='add',
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seasonal='add',
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seasonal_periods=12)
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model_fit = model.fit()
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forecast = model_fit.forecast(forecast_periods)
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model_params = "Additive Trend, Additive Seasonal"
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# Calculate metrics on test data if available
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if test_size > 0:
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test_predictions = model_fit.forecast(test_size)
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mae = mean_absolute_error(test_data, test_predictions)
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mse = mean_squared_error(test_data, test_predictions)
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rmse = np.sqrt(mse)
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metrics = {'MAE': mae, 'MSE': mse, 'RMSE': rmse}
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elif model_type == 'Prophet':
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# Prepare data for Prophet
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prophet_df = train_data.reset_index().rename(columns={date_col: 'ds', value_col: 'y'})
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model = Prophet(yearly_seasonality=True, weekly_seasonality=False, daily_seasonality=False)
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model.add_seasonality(name='monthly', period=30.5, fourier_order=5)
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model_fit = model.fit(prophet_df)
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# Create future dataframe
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future = model.make_future_dataframe(periods=forecast_periods, freq=df.index.inferred_freq)
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forecast_full = model_fit.predict(future)
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forecast = forecast_full['yhat'].iloc[-forecast_periods:].values
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model_params = "Prophet"
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# Calculate metrics on test data if available
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if test_size > 0:
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test_future = model.make_future_dataframe(periods=test_size, freq=df.index.inferred_freq)
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test_predictions = model.predict(test_future)['yhat'].iloc[-test_size:].values
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mae = mean_absolute_error(test_data, test_predictions)
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mse = mean_squared_error(test_data, test_predictions)
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rmse = np.sqrt(mse)
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metrics = {'MAE': mae, 'MSE': mse, 'RMSE': rmse}
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# Forecast plot
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forecast_fig = go.Figure()
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forecast_fig.add_trace(go.Scatter(x=df.index, y=df[value_col], name='Historical'))
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if test_size > 0:
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forecast_fig.add_trace(
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go.Scatter(x=df.index[train_size:], y=test_data, name='Test Data', line=dict(color='green')))
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forecast_fig.add_trace(
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go.Scatter(x=forecast_dates, y=forecast, name=f'Forecast ({model_type})', line=dict(dash='dash')))
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forecast_fig.update_layout(title=f'Forecast ({model_type})', height=400)
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forecast_html = pio.to_html(forecast_fig, full_html=False)
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# ACF/PACF plots
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if do_acf_pacf:
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acf_pacf_html = create_acf_pacf_plots(df[value_col])
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# Save processed data
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filename = save_processed_file(processed_df, filepath)
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return {
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'plot_html': plot_html,
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'forecast_html': forecast_html,
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'acf_pacf_html': acf_pacf_html,
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'summary': summary,
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'filename': filename,
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'model_params': model_params,
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'train_size': train_size,
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'test_size': test_size,
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'metrics': metrics,
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'forecast_dates': forecast_dates.tolist() if do_forecasting else [],
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'forecast_values': forecast.tolist() if do_forecasting else [],
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'model_type': model_type
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}
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except Exception as e:
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return {'error': str(e)}
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