Line plots are a great way to visualize data trends over time. Matplotlib offers various functionalities to incorporate time-series data into your plots. Let's say we have a dataset of daily stock prices of a company. We can use Matplotlib to plot the closing prices over a period of time.

import matplotlib.pyplot as plt

dates = ['2021-01-01', '2021-01-02', ...]
closing_prices = [100, 110, ...]

plt.plot(dates, closing_prices)
plt.xlabel('Date')
plt.ylabel('Closing Price ($)')
plt.title('Stock Price Trend')
plt.show()

This simple line plot will display the trend of the closing prices over time.

To visualize the overall trend, we can use an area plot. The area plot fills the space below the line with a color, representing the accumulated value.

plt.fill_between(dates, closing_prices)
plt.xlabel('Date')
plt.ylabel('Closing Price ($)')
plt.title('Stock Price Trend')
plt.show()

This area plot provides a clearer view of the overall trend in stock prices.

Matplotlib also supports the usage of error bars to represent statistical data, such as mean and standard deviation. We can plot a line graph with error bars to display the mean closing prices and the variability.

plt.errorbar(dates, closing_prices, yerr=std_dev)
plt.xlabel('Date')
plt.ylabel('Closing Price ($)')
plt.title('Stock Price Trend with Error Bars')
plt.show()

By incorporating error bars, we can instantly identify the range of closing prices and visualize the level of variability in the dataset.