Time series data is widely present in our lives. Stock prices, house prices, weather information, and sales data captured over time are just a few examples. As businesses increasingly look for new ways to gain meaningful insights from time-series data, the ability to visualize data and apply desired transformations are fundamental steps. However, time-series data possesses unique characteristics and nuances compared to other kinds of tabular data, and require special considerations. For example, standard tabular or cross-sectional data is collected at a specific point in time. In contrast, time series data is captured repeatedly over time, with each successive data point dependent on its past values.
Because most time series analyses rely on the information gathered across a contiguous set of observations, missing data and inherent sparseness can reduce the accuracy of forecasts and introduce bias. Additionally, most time series analysis approaches rely on equal spacing between data points, in

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