Editing Time Series (section)

== <span style="color: #FFFFFF;">Applying</span> ==
'''Calculating a Simple Moving Average (SMA):'''
<syntaxhighlight lang="python">
def simple_moving_average(data, window_size):
    """
    Smooths out a time series by averaging over a window.
    """
    averages = []
    for i in range(len(data) - window_size + 1):
        window = data[i : i + window_size]
        averages.append(sum(window) / window_size)
    return averages

# Stock prices over 10 days
prices = [100, 102, 101, 105, 110, 108, 115, 120, 118, 125]
sma_3 = simple_moving_average(prices, 3)

print(f"Original Prices: {prices}")
print(f"3-Day SMA:       {sma_3}")
# Note how the 'zig-zags' are smoothed out, making the upward 
# trend easier to see.
</syntaxhighlight>

; Time Series in the Real World
: '''Stock Market''' → Analyzing price "momentum" and volatility.
: '''Epidemiology''' → Tracking the "R-number" (spread of a virus) over time.
: '''IoT / Sensors''' → Detecting an "Anomaly" (e.g., a machine vibrating weirdly) in a constant stream of data.
: '''Inventory Management''' → Predicting how many turkeys to stock before Thanksgiving based on the last 5 years of data.
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