Quantitative variables have numerical values and can be quantified/measured on a discrete or continuous scale. Generally, time-series data can be divided into two types of variables, i.e., quantitative and qualitative. When plotting a time-series chart, it is important to consider the type of variable you are plotting. A time plot with time on x-axis and temperature on y-axis can be used to visualize this data.Ĭheck out our earlier blog post to learn more about time-series data. We can take temperature readings at regular intervals to identify patterns in temperature changes over time. A time plot with time on x-axis and the stock price on y-axis can be used to visualize this data.Īnother great example would be temperature readings over time. Regarding stock prices, we can also collect them at regular intervals i.e., daily, hourly, or every minute to identify trends over time. A time plot with time on x-axis and the number of website visitors on y-axis can be used to visualize this data. We can collect website traffic data using tools such as Google Analytics and analyze it to identify trends in user behavior over time. The two previously mentioned examples- website traffic and stock prices over time-are time-series data use cases. You can visualize this dependency via a time plot that displays data points collected in a time sequence. It means that each data point is dependent on the one that came before it. One of the most significant features of time-series data is its sequential nature. Time-series data is a type of data collected at regular intervals over time. The fluctuations in the stock price over time may be due to a variety of factors, such as changes in market conditions, company financial performance, or industry trends. stock (AAPL) over the past year:īy analyzing the plot, we can see that there are periods of both upward and downward trends in the stock price. Here's an example time-series plot of the daily closing prices of Apple Inc. You can create a time-series plot of the daily closing prices of the stock, with the x-axis representing time (in days) and the y-axis representing the stock's closing price. Let's say you want to visualize the stock prices of a particular company over the past year. The graph also shows some fluctuations in traffic, but overall the trend is a seasonal pattern of higher traffic during the summer months. The traffic then gradually declines towards the end of the year, with a sharp decrease in traffic during the last two months. The data shows a higher traffic pattern during the year's middle months, with the peak in traffic occurring in August. Here's an example time plot of the monthly website traffic to a fictional website over the past year: You can create a time-series plot of the daily or monthly website visits, with the x-axis representing time and the y-axis representing the number of visits. Let's say you want to analyze the traffic to your website over the past year. This way, you can identify seasonal fluctuations, long-term trends, and cyclic patterns in data. For instance, you can see how a particular variable changes over months, seasons, years, or even decades. Time-series plots allow you to see trends and patterns in data that might not be visible in other types of graphs. We use time plots in many fields, such as economics, finance, engineering, and meteorology, to visualize and analyze changes over time. In a time-series plot, the x-axis represents the time, and the y-axis represents the variable being measured. What Is a Time-Series Plot?Ī time-series plot, also known as a time plot, is a type of graph that displays data points collected in a time sequence. Understanding how to create and interpret time-series plots is essential as they help us make better decisions and stay ahead of the competition. In this article, we will take a deep dive into time-series plots, exploring what they are and how you can use them to extract valuable information from your data. It helps uncover hidden patterns in your data so that you can gain insights into trends, cycles, and fluctuations over time. Have you ever marveled at what the future holds? Whether you are developing a crypto trading platform, collecting data from IoT devices to measure energy consumption, or working on an application to forecast sales, a time-series plot is an indispensable tool for predicting the future.
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