If you're working with time series forecasting in Python, you may encounter an issue where the StatsForecast plotting feature doesn't display plots when used inside a loop. This can be frustrating, especially when you're trying to visualize multiple forecasts in a single execution. In this article, we will analyze this problem, correct the scenario, and provide practical solutions to ensure that your plots display correctly.
Original Problem Scenario
The original problem statement might look something like this:
"I can't get the Statsforecast plot feature to display inside a for loop."
Corrected Sentence:
"I am unable to display the StatsForecast plot feature within a for loop."
Understanding the Issue
When attempting to plot forecasts using the StatsForecast package in Python within a for loop, you may not see the expected output. This can be attributed to how Python's plotting libraries manage the figure context in loops.
Here's a simple example of how you might be attempting to plot inside a for loop:
import pandas as pd
from statsforecast import StatsForecast
from statsforecast.models import AutoARIMA
import matplotlib.pyplot as plt
# Sample DataFrame
df = pd.DataFrame({
'time': pd.date_range(start='2020-01-01', periods=100),
'value': np.random.rand(100)
})
model = StatsForecast(df, 'value', models=[AutoARIMA()], freq='D')
for i in range(5): # Looping through forecast horizons
model.plot(horizon=i)
Why Plots Might Not Display
When running the above code, you might notice that the plots do not render as expected. This could be because:
- Matplotlib's Behavior: Matplotlib needs to be told to show plots explicitly when called within a loop. If not, it won't display the figure until the end of the execution.
- Overriding Figures: Each plot call might be overriding the previous one if they are not being saved or properly closed.
Solutions and Best Practices
To ensure your plots display correctly, consider the following approaches:
Use plt.show() in the Loop
Add plt.show() after each plot command to explicitly tell Matplotlib to render the plot:
for i in range(5): # Looping through forecast horizons
model.plot(horizon=i)
plt.show() # Show the plot for each horizon
Saving Plots to Files
If you prefer to save your plots instead of displaying them, you can use plt.savefig():
for i in range(5):
model.plot(horizon=i)
plt.savefig(f'forecast_horizon_{i}.png') # Save each plot as a file
plt.close() # Close the plot to avoid overlapping
Creating Subplots
If you want to visualize multiple forecasts in a single window, consider creating subplots:
fig, axs = plt.subplots(3, 2, figsize=(12, 8)) # Create a grid of subplots
for i in range(6): # Adjust number of plots as needed
ax = axs[i//2, i%2] # Select subplot
model.plot(horizon=i, ax=ax) # Plot on the selected axis
plt.tight_layout() # Adjust layout
plt.show() # Show all plots together
Conclusion
In summary, plotting within a loop in Python using StatsForecast can lead to challenges, but with the right adjustments, you can effectively visualize your forecasts. Remember to include plt.show() after each plot in a loop, consider saving your plots, or use subplots for better organization.
Additional Resources
By following the tips provided, you can enhance your time series forecasting visualizations and improve your analytical skills in Python. Happy forecasting!
