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This article is part of the Tool Mastery Series, a compilation of Knowledge Base contributions to introduce diverse working examples for Designer Tools. Here we’ll delve into uses of the Time Series Plot Tool on our way to mastering the Alteryx Designer:
Time-series forecasting is the use of a statistical model to predict future values based on past results. So what can these future and past values be? Any variable that can be tracked and collected over time! Think of annual population data, a company's daily stock price, or quarterly sales figures. For our examples we will be working with bookings data gathered from a mountain resort hotel.
How should this data be collected?
Over a continuous time interval
Of sequential measurements across that interval
Using equal spacing between every two consecutive measurements and
With each time unit within the time interval having at most one data point
Once the data has been structured to fit this format, Atleryx has a host of Time Series tools to help you display, analyze and predict future values. This is done with the same easy drag and drop interface that you've come to know and love!
Let's take a look at these tools a bit more in depth...
TS Plot - This tool provides a number of different univariate time series plots that are useful in both better understanding the time series data and determining how to proceed in developing a forecasting model.
Key Terms to Understand:
Trend - A gradual shift or movement to relatively higher or lower values over a long period of time. A series can exhibit uptrends, sideways trends, downtrends or even a combination of all three! Trends can be good or bad regardless of whether its an uptrend or downtrend. Think of an uptrend in sales. Good! Now think about an uptrend in CO2 emissions. That's bad! Knowing the trend and your data will provide better insight.
Seasonality - Arepeating pattern at fixed intervals of time within a one year. The image below tracks monthly champagne sales. We see seasonal peaks exhibited in the month of December. Let's ring in the new year!
Cyclical Cycles - A time series that exhibits rises and falls that are not of a fixed period. Think of business cycles which usually last several years, but where the length of the current cycle is unknown beforehand. In finance, times of expansion and recession in the stock market reveals cyclical patterns. A cyclical uptrend is referred to as a bull market, while a cyclical downtrend, is referred to as a bear market.
Outlier - A data point on a graph or in a set of results that is very much bigger or smaller than the next nearest data point
TS Plot Configuration Properties:
Target Field: Select the field from the data stream for which you wish to create a time series plot. Measurements for this field need to be made at regular time intervals (e.g., daily, monthly, quarterly, etc.).
Target field frequency: Choose the time interval for the observations of the target field.
Series starting period (optional): This option allows the user to specify the starting period of the time series, which will be reflected in the forecast plot.
Time Series Plot - A graphical presentation of the relationship between time and the time series target variable; time is on the horizontal axis and the target variable’s values are shown on the vertical axis. Help assess whether the original time series needs to be transformed and whether there are outliers in the series. Also, shows trend, seasonality and cyclical patterns in the time series.
Seasonal Plot - Allows for an assessment of the existence and nature of any seasonality in the series. All the monthly values are plotted in chronological order with each month on the horizontal axis. The years represent the 10 separate years of data in the time series. We can see that our data has seasonal spikes during the summer and winter months.
Seasonal Deviation Plot - Allows for an assessment of whether the underlying nature of the time series varies across seasons. Our bookings series shows that January, February and August have the greatest deviations.
Autoregression Function and Partial Autoregression Function Plots - Determine the nature of any underlying autocorrelation and in assessing the possible needs in terms of data differencing for the creation of an ARIMA model. The lag refers to the number of periods behind the most recent period and the correlation coefficient is the vertical axis.
Time Series Decomposition Plot - A visual examination of the original data, the trend in the data, the seasonality in the data, and information about the residuals once seasonality and trend have been take into account. We see an upward trend in our data with exponentially increasing seasonality as time moves on.The remainder shows an exponential increase in the residual data.
By now, you should have expert-level proficiency with the Time Series Plot Tool! If you can think of a use case we left out, feel free to use the comments section below! Consider yourself a Tool Master already? Let us know at firstname.lastname@example.org you’d like your creative tool uses to be featured in the Tool Mastery Series.
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