Regarding smoothing methods on time series, which statement is true?

The correct statement about smoothing methods on time series is that larger k values yield smoother series. This is based on the principle that increasing the value of k, which indicates the number of observations included in the moving average, results in averaging over a broader range of data points. As a result, short-term fluctuations or noise within the time series are dampened, providing a clearer understanding of the underlying trend or pattern.

In time series analysis, smoothing is essential for distinguishing significant trends from random variations. The larger k value essentially means you are taking into account more data points, thereby reducing the impact of transient spikes or drops in the data, leading to a smoother output. This smoothing effect is crucial for forecasting and decision-making based on the data.

Overall, this understanding underscores the importance of the choice of k in moving averages and highlights how it influences the clarity of the time series analysis.