Time series data A time series is a collection of data points gathered over a period of time and ordered chronologically. The primary characteristic of a time series is that it’s indexed or listed in time order, which is a critical distinction from other types of data sets. If you were to plot the points of time series data on a graph, and one of your axes would always be time. Time series metrics refer to a piece of data that is tracked at an increment in time. For instance, a metric could refer to how much inventory was sold in a store from one day to the next. Time series data is everywhere, since time is a constituent of everything that is observable. As our world gets increasingly instrumented, sensors and systems are constantly emitting a relentless stream of time series data. Such data has numerous applications across various industries. Let’s put this in context through some examples. Examples of time series analysis: Electrical activity in the brain Rainfall measurements Stock...
Color is a powerful tool in data visualization. It can be used to: Differentiate between categories: Colors can be assigned to different categories of data to make them visually distinct. For example, in a bar chart, you might use different colors to represent different product categories. Bar chart with different colors for product categories Represent quantitative values: Colors can be used to represent the magnitude of a quantitative variable. For example, in a heatmap, you might use a gradient of colors from red to blue to represent temperature, with red representing higher temperatures and blue representing lower temperatures. Heatmap with gradient colors representing temperature Highlight trends and patterns: Colors can be used to highlight trends and patterns in your data. For example, in a line chart, you might use a different color for each line to show how different variables change over time. Line chart with different colors for trend lines However, it's important to use...
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