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import gradio as gr
import pandas as pd
import pmdarima as pm
from pmdarima import auto_arima
import plotly.graph_objs as go
def forecast_time_series(file):
# Load data
data = pd.read_csv(file.name, skiprows=2)
period_type = data.columns[0]
data[period_type] = pd.to_datetime(data[period_type])
data.set_index(period_type, inplace=True)
# Fit the SARIMAX model automatically
model = auto_arima(data)
# Forecasting
n_periods = 24 # Number of periods to forecast
forecast, conf_int = model.predict(n_periods=n_periods, return_conf_int=True)
# Create a DataFrame with the forecast and confidence intervals
forecast_index = pd.date_range(start=data.index[-1], periods=n_periods + 1, freq=data.index.inferred_freq)[1:]
forecast_df = pd.DataFrame(forecast, index=forecast_index, columns=['Forecast'])
conf_int_df = pd.DataFrame(conf_int, index=forecast_index, columns=['Lower CI', 'Upper CI'])
forecast_df = pd.concat([forecast_df, conf_int_df], axis=1)
# Calculate the YoY change
sum_last_12_original = data.iloc[-12:, 0].sum()
sum_first_12_forecast = forecast_df['Forecast'].iloc[:12].sum()
yoy_change = (sum_first_12_forecast - sum_last_12_original) / sum_last_12_original
# Create an interactive plot with Plotly
fig = go.Figure()
fig.add_trace(go.Scatter(x=data.index, y=data.iloc[:, 0], mode='lines', name='Original Series'))
fig.add_trace(go.Scatter(x=forecast_df.index, y=forecast_df['Forecast'], mode='lines', name='Forecast', line=dict(color='red')))
fig.add_trace(go.Scatter(
x=forecast_df.index,
y=forecast_df['Lower CI'],
fill=None,
mode='lines',
line=dict(color='pink'),
showlegend=False
))
fig.add_trace(go.Scatter(
x=forecast_df.index,
y=forecast_df['Upper CI'],
fill='tonexty',
mode='lines',
line=dict(color='pink'),
name='Confidence Interval'
))
fig.update_layout(
title='Original Time Series and Forecast with Confidence Intervals',
xaxis_title='Date',
yaxis_title='Values',
legend=dict(
orientation='h',
yanchor='bottom',
y=1.02,
xanchor='right',
x=1
),
hovermode='x unified'
)
# Combine original data and forecast data into one DataFrame for export
combined_df = pd.concat([data, forecast_df], axis=0)
combined_file = 'combined_data.csv'
combined_df.to_csv(combined_file)
# Return plot file path and YoY change
return fig, f'Year-over-Year Change in Sum of Values: {yoy_change:.2%}', combined_file
# Create Gradio interface using Blocks
with gr.Blocks(theme=gr.themes.Monochrome()) as interface:
gr.Markdown("# Time Series Forecasting")
gr.Markdown("Upload a CSV file with a time series to forecast the next 24 periods and see the YoY % change. Download the combined original and forecast data.")
with gr.Row():
file_input = gr.File(label="Upload Time Series CSV")
with gr.Row():
plot_output = gr.Plot(label="Time Series + Forecast Chart")
with gr.Row():
yoy_output = gr.Text(label="YoY % Change")
with gr.Row():
csv_output = gr.File(label="Download Combined Data CSV")
file_input.change(forecast_time_series, inputs=file_input, outputs=[plot_output, yoy_output, csv_output])
# Launch the interface
interface.launch()
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