some new features
This commit is contained in:
ilgazca
1
models/__init__.py
Normal file
1
models/__init__.py
Normal file
@ -0,0 +1 @@
|
||||
# Empty __init__.py to make models a package
|
||||
BIN
models/__pycache__/__init__.cpython-312.pyc
Normal file
BIN
models/__pycache__/__init__.cpython-312.pyc
Normal file
Binary file not shown.
BIN
models/__pycache__/plotting.cpython-312.pyc
Normal file
BIN
models/__pycache__/plotting.cpython-312.pyc
Normal file
Binary file not shown.
BIN
models/__pycache__/time_series.cpython-312.pyc
Normal file
BIN
models/__pycache__/time_series.cpython-312.pyc
Normal file
Binary file not shown.
138
models/plotting.py
Normal file
138
models/plotting.py
Normal file
@ -0,0 +1,138 @@
|
||||
import pandas as pd
|
||||
import plotly.express as px
|
||||
import plotly.graph_objects as go
|
||||
import plotly.io as pio
|
||||
from statsmodels.graphics.tsaplots import plot_acf, plot_pacf
|
||||
import matplotlib
|
||||
|
||||
matplotlib.use('Agg')
|
||||
import matplotlib.pyplot as plt
|
||||
import io
|
||||
import base64
|
||||
from statsmodels.tsa.holtwinters import ExponentialSmoothing
|
||||
import pmdarima as pm
|
||||
from prophet import Prophet
|
||||
|
||||
|
||||
def create_acf_pacf_plots(data):
|
||||
# Create ACF and PACF plots using matplotlib
|
||||
fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(10, 8))
|
||||
|
||||
plot_acf(data, ax=ax1, lags=40)
|
||||
ax1.set_title('Autocorrelation Function')
|
||||
|
||||
plot_pacf(data, ax=ax2, lags=40)
|
||||
ax2.set_title('Partial Autocorrelation Function')
|
||||
|
||||
# Convert matplotlib plot to Plotly
|
||||
buf = io.BytesIO()
|
||||
plt.savefig(buf, format='png')
|
||||
plt.close(fig)
|
||||
buf.seek(0)
|
||||
img_str = base64.b64encode(buf.getvalue()).decode('utf-8')
|
||||
|
||||
# Create Plotly figure with image
|
||||
fig_plotly = go.Figure()
|
||||
fig_plotly.add_layout_image(
|
||||
dict(
|
||||
source=f'data:image/png;base64,{img_str}',
|
||||
x=0,
|
||||
y=1,
|
||||
xref="paper",
|
||||
yref="paper",
|
||||
sizex=1,
|
||||
sizey=1,
|
||||
sizing="stretch",
|
||||
opacity=1,
|
||||
layer="below"
|
||||
)
|
||||
)
|
||||
fig_plotly.update_layout(
|
||||
height=600,
|
||||
showlegend=False,
|
||||
xaxis=dict(visible=False),
|
||||
yaxis=dict(visible=False)
|
||||
)
|
||||
return pio.to_html(fig_plotly, full_html=False)
|
||||
|
||||
|
||||
def create_comparison_plot(filepath, forecast_history, selected_indices):
|
||||
# Read data
|
||||
if filepath.endswith('.csv'):
|
||||
df = pd.read_csv(filepath)
|
||||
else:
|
||||
df = pd.read_excel(filepath)
|
||||
|
||||
date_col = df.columns[0]
|
||||
value_col = df.columns[1]
|
||||
df[date_col] = pd.to_datetime(df[date_col])
|
||||
df.set_index(date_col, inplace=True)
|
||||
|
||||
# Create Plotly figure
|
||||
fig = go.Figure()
|
||||
fig.add_trace(go.Scatter(x=df.index, y=df[value_col], name='Historical', line=dict(color='black')))
|
||||
|
||||
# Use Plotly qualitative colors
|
||||
colors = px.colors.qualitative.Plotly
|
||||
|
||||
# Generate forecasts for selected indices
|
||||
for idx, run_idx in enumerate(selected_indices):
|
||||
entry = forecast_history[run_idx]
|
||||
train_percent = entry['train_percent'] / 100
|
||||
forecast_periods = entry['forecast_periods']
|
||||
model_type = entry['model_type']
|
||||
|
||||
# Split data
|
||||
train_size = int(len(df) * train_percent)
|
||||
test_size = len(df) - train_size
|
||||
train_data = df[value_col].iloc[:train_size]
|
||||
test_data = df[value_col].iloc[train_size:] if test_size > 0 else pd.Series()
|
||||
forecast_dates = pd.date_range(start=df.index[-1], periods=forecast_periods + 1, freq=df.index.inferred_freq)[
|
||||
1:]
|
||||
|
||||
# Run model based on model_type
|
||||
forecast = None
|
||||
if model_type == 'ARIMA':
|
||||
model = pm.auto_arima(train_data,
|
||||
seasonal=True,
|
||||
m=12,
|
||||
start_p=0, start_q=0,
|
||||
max_p=3, max_q=3,
|
||||
start_P=0, start_Q=0,
|
||||
max_P=2, max_Q=2,
|
||||
d=1, D=1,
|
||||
trace=False,
|
||||
error_action='ignore',
|
||||
suppress_warnings=True,
|
||||
stepwise=True)
|
||||
model_fit = model.fit(train_data)
|
||||
forecast = model_fit.predict(n_periods=forecast_periods)
|
||||
|
||||
elif model_type == 'Exponential Smoothing':
|
||||
model = ExponentialSmoothing(train_data,
|
||||
trend='add',
|
||||
seasonal='add',
|
||||
seasonal_periods=12)
|
||||
model_fit = model.fit()
|
||||
forecast = model_fit.forecast(forecast_periods)
|
||||
|
||||
elif model_type == 'Prophet':
|
||||
prophet_df = train_data.reset_index().rename(columns={date_col: 'ds', value_col: 'y'})
|
||||
model = Prophet(yearly_seasonality=True, weekly_seasonality=False, daily_seasonality=False)
|
||||
model.add_seasonality(name='monthly', period=30.5, fourier_order=5)
|
||||
model_fit = model.fit(prophet_df)
|
||||
future = model.make_future_dataframe(periods=forecast_periods, freq=df.index.inferred_freq)
|
||||
forecast_full = model_fit.predict(future)
|
||||
forecast = forecast_full['yhat'].iloc[-forecast_periods:].values
|
||||
|
||||
# Add test data if available (only once to avoid clutter)
|
||||
if test_size > 0 and idx == 0:
|
||||
fig.add_trace(go.Scatter(x=df.index[train_size:], y=test_data, name='Test Data', line=dict(color='green')))
|
||||
|
||||
# Add forecast
|
||||
label = f"Forecast Run {run_idx + 1}: {model_type}, {entry['train_percent']:.0f}/{entry['test_percent']:.0f}, {forecast_periods} periods"
|
||||
fig.add_trace(go.Scatter(x=forecast_dates, y=forecast, name=label,
|
||||
line=dict(dash='dash', color=colors[idx % len(colors)])))
|
||||
|
||||
fig.update_layout(title='Forecast Comparison', height=400, showlegend=True)
|
||||
return pio.to_html(fig, full_html=False)
|
||||
176
models/time_series.py
Normal file
176
models/time_series.py
Normal file
@ -0,0 +1,176 @@
|
||||
import pandas as pd
|
||||
from statsmodels.tsa.seasonal import seasonal_decompose
|
||||
from statsmodels.tsa.holtwinters import ExponentialSmoothing
|
||||
import pmdarima as pm
|
||||
from prophet import Prophet
|
||||
import plotly.express as px
|
||||
import plotly.graph_objects as go
|
||||
from plotly.subplots import make_subplots
|
||||
import plotly.io as pio
|
||||
import numpy as np
|
||||
from sklearn.metrics import mean_absolute_error, mean_squared_error
|
||||
from utils.file_handling import save_processed_file
|
||||
from .plotting import create_acf_pacf_plots
|
||||
|
||||
|
||||
def process_time_series(filepath, do_decomposition, do_forecasting, do_acf_pacf, train_percent, forecast_periods,
|
||||
model_type):
|
||||
try:
|
||||
# Read file
|
||||
if filepath.endswith('.csv'):
|
||||
df = pd.read_csv(filepath)
|
||||
else:
|
||||
df = pd.read_excel(filepath)
|
||||
|
||||
# Ensure datetime column exists
|
||||
date_col = df.columns[0] # Assume first column is date
|
||||
value_col = df.columns[1] # Assume second column is value
|
||||
df[date_col] = pd.to_datetime(df[date_col])
|
||||
df.set_index(date_col, inplace=True)
|
||||
|
||||
# Initialize variables
|
||||
plot_html = None
|
||||
forecast_html = None
|
||||
acf_pacf_html = None
|
||||
summary = df[value_col].describe().to_dict()
|
||||
model_params = None
|
||||
train_size = None
|
||||
test_size = None
|
||||
metrics = None
|
||||
|
||||
# Save processed data
|
||||
processed_df = df.copy()
|
||||
|
||||
# Time series decomposition
|
||||
if do_decomposition:
|
||||
decomposition = seasonal_decompose(df[value_col], model='additive', period=12)
|
||||
fig = make_subplots(rows=4, cols=1,
|
||||
subplot_titles=('Original Series', 'Trend', 'Seasonality', 'Residuals'))
|
||||
|
||||
fig.add_trace(go.Scatter(x=df.index, y=df[value_col], name='Original'), row=1, col=1)
|
||||
fig.add_trace(go.Scatter(x=df.index, y=decomposition.trend, name='Trend'), row=2, col=1)
|
||||
fig.add_trace(go.Scatter(x=df.index, y=decomposition.seasonal, name='Seasonality'), row=3, col=1)
|
||||
fig.add_trace(go.Scatter(x=df.index, y=decomposition.resid, name='Residuals'), row=4, col=1)
|
||||
|
||||
fig.update_layout(height=800, showlegend=True)
|
||||
plot_html = pio.to_html(fig, full_html=False)
|
||||
|
||||
processed_df['Trend'] = decomposition.trend
|
||||
processed_df['Seasonality'] = decomposition.seasonal
|
||||
processed_df['Residuals'] = decomposition.resid
|
||||
|
||||
# Forecasting
|
||||
if do_forecasting:
|
||||
# Split data into train and test
|
||||
train_size = int(len(df) * train_percent)
|
||||
test_size = len(df) - train_size
|
||||
train_data = df[value_col].iloc[:train_size]
|
||||
test_data = df[value_col].iloc[train_size:] if test_size > 0 else pd.Series()
|
||||
forecast_dates = pd.date_range(start=df.index[-1], periods=forecast_periods + 1,
|
||||
freq=df.index.inferred_freq)[1:]
|
||||
|
||||
# Initialize forecast and model parameters
|
||||
forecast = None
|
||||
if model_type == 'ARIMA':
|
||||
# Auto ARIMA for best parameters
|
||||
model = pm.auto_arima(train_data,
|
||||
seasonal=True,
|
||||
m=12,
|
||||
start_p=0, start_q=0,
|
||||
max_p=3, max_q=3,
|
||||
start_P=0, start_Q=0,
|
||||
max_P=2, max_Q=2,
|
||||
d=1, D=1,
|
||||
trace=False,
|
||||
error_action='ignore',
|
||||
suppress_warnings=True,
|
||||
stepwise=True)
|
||||
|
||||
# Fit ARIMA with best parameters
|
||||
model_fit = model.fit(train_data)
|
||||
forecast = model_fit.predict(n_periods=forecast_periods)
|
||||
model_params = f"{model.order}, Seasonal{model.seasonal_order}"
|
||||
|
||||
# Calculate metrics on test data if available
|
||||
if test_size > 0:
|
||||
test_predictions = model_fit.predict(n_periods=test_size)
|
||||
mae = mean_absolute_error(test_data, test_predictions)
|
||||
mse = mean_squared_error(test_data, test_predictions)
|
||||
rmse = np.sqrt(mse)
|
||||
metrics = {'MAE': mae, 'MSE': mse, 'RMSE': rmse}
|
||||
|
||||
elif model_type == 'Exponential Smoothing':
|
||||
# Fit Exponential Smoothing model
|
||||
model = ExponentialSmoothing(train_data,
|
||||
trend='add',
|
||||
seasonal='add',
|
||||
seasonal_periods=12)
|
||||
model_fit = model.fit()
|
||||
forecast = model_fit.forecast(forecast_periods)
|
||||
model_params = "Additive Trend, Additive Seasonal"
|
||||
|
||||
# Calculate metrics on test data if available
|
||||
if test_size > 0:
|
||||
test_predictions = model_fit.forecast(test_size)
|
||||
mae = mean_absolute_error(test_data, test_predictions)
|
||||
mse = mean_squared_error(test_data, test_predictions)
|
||||
rmse = np.sqrt(mse)
|
||||
metrics = {'MAE': mae, 'MSE': mse, 'RMSE': rmse}
|
||||
|
||||
elif model_type == 'Prophet':
|
||||
# Prepare data for Prophet
|
||||
prophet_df = train_data.reset_index().rename(columns={date_col: 'ds', value_col: 'y'})
|
||||
model = Prophet(yearly_seasonality=True, weekly_seasonality=False, daily_seasonality=False)
|
||||
model.add_seasonality(name='monthly', period=30.5, fourier_order=5)
|
||||
model_fit = model.fit(prophet_df)
|
||||
|
||||
# Create future dataframe
|
||||
future = model.make_future_dataframe(periods=forecast_periods, freq=df.index.inferred_freq)
|
||||
forecast_full = model_fit.predict(future)
|
||||
forecast = forecast_full['yhat'].iloc[-forecast_periods:].values
|
||||
model_params = "Prophet"
|
||||
|
||||
# Calculate metrics on test data if available
|
||||
if test_size > 0:
|
||||
test_future = model.make_future_dataframe(periods=test_size, freq=df.index.inferred_freq)
|
||||
test_predictions = model.predict(test_future)['yhat'].iloc[-test_size:].values
|
||||
mae = mean_absolute_error(test_data, test_predictions)
|
||||
mse = mean_squared_error(test_data, test_predictions)
|
||||
rmse = np.sqrt(mse)
|
||||
metrics = {'MAE': mae, 'MSE': mse, 'RMSE': rmse}
|
||||
|
||||
# Forecast plot
|
||||
forecast_fig = go.Figure()
|
||||
forecast_fig.add_trace(go.Scatter(x=df.index, y=df[value_col], name='Historical'))
|
||||
if test_size > 0:
|
||||
forecast_fig.add_trace(
|
||||
go.Scatter(x=df.index[train_size:], y=test_data, name='Test Data', line=dict(color='green')))
|
||||
forecast_fig.add_trace(
|
||||
go.Scatter(x=forecast_dates, y=forecast, name=f'Forecast ({model_type})', line=dict(dash='dash')))
|
||||
forecast_fig.update_layout(title=f'Forecast ({model_type})', height=400)
|
||||
forecast_html = pio.to_html(forecast_fig, full_html=False)
|
||||
|
||||
# ACF/PACF plots
|
||||
if do_acf_pacf:
|
||||
acf_pacf_html = create_acf_pacf_plots(df[value_col])
|
||||
|
||||
# Save processed data
|
||||
filename = save_processed_file(processed_df, filepath)
|
||||
|
||||
return {
|
||||
'plot_html': plot_html,
|
||||
'forecast_html': forecast_html,
|
||||
'acf_pacf_html': acf_pacf_html,
|
||||
'summary': summary,
|
||||
'filename': filename,
|
||||
'model_params': model_params,
|
||||
'train_size': train_size,
|
||||
'test_size': test_size,
|
||||
'metrics': metrics,
|
||||
'forecast_dates': forecast_dates.tolist() if do_forecasting else [],
|
||||
'forecast_values': forecast.tolist() if do_forecasting else [],
|
||||
'model_type': model_type
|
||||
}
|
||||
|
||||
except Exception as e:
|
||||
return {'error': str(e)}
|
||||
Reference in New Issue
Block a user