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# Time Series Forecasting in Alteryx

Aug 5, 2019

Making predictions about the future is called extrapolation in the classical statistical handling of time series data. Forecasting involves taking models fit on historical data and using them to predict future observations.

Alteryx provides a list of pre-built tools under Predictive and Time series categories for predicting and forecasting the data based on our current performance. Where the Predictive tab holds the R-based tools and the Time series tab hold the tools which can be used to predict based on time component. Let’s look more about the time series forecasting methods available in Alteryx.

Time series analysis involves developing models that best capture or describe an observed time series in order to understand the underlying causes. This field of study seeks the “why” behind a time series dataset. This often involves making assumptions about the form of the data and decomposing the time series into constitution components.

In Alteryx, we have two prebuilt tools to perform a time series forecast.

1. ARIMA tool
2. ETS tool

### ARIMA Model:

ARIMA stands for Auto Regressive Integrated Moving Average. There are seasonal and Non-seasonal ARIMA models that can be used for forecasting.

#### Variables in Non-seasonal Model: ARIMA(p,d,q)

This method has three variables to account for

• p = Periods to lag for ex: if P= 3 then we will use the three previous periods of our time series in the autoregressive portion of the calculation
• d = In an ARIMA model we transform a time series into stationary data (series without trend or seasonality) using differencing. D refers to the number of differencing transformations required by the time series to get stationary.
• q = This variable denotes the lag of the error component, where error component is a part of the time series not explained by trend or seasonality

#### Variables in Seasonal Model: ARIMA(p,d,q)(P, D, Q)m

• m — refers to the number of periods in each season
• (P, D, Q)— represents the (p,d,q) for the seasonal part of the time series

#### Forecasting by ARIMA tool:

The ARIMA tool estimates a time series forecasting model, either as a univariate model or one with covariates (predictors), using an autoregressive integrated moving average (or ARIMA) method.

Customize the parameters (p,d,q,P,D,Q) based on the required model to forecast the data. Provide the time period to forecast. The model will be ready when we run the workflow.

### ETS Model: (Error, Trend, Season)

Exponential smoothing is a time series forecasting method for univariate data that can be extended to support data with a systematic trend or seasonal component.

Exponential smoothing forecasting methods are similar in that a prediction is a weighted sum of past observations like ARIMA, but the model explicitly uses an exponentially decreasing weight for past observations.

This method has three variables to account for

• Error
• Trend (You can specify dampening for the trend, if needed)
• Season

You must define the type in which these three variables need to process as Additive type or Multiplicative type.

#### Forecasting by ETS Tool:

The ETS tool estimates a univariate time series forecasting model using an exponential smoothing method.

Configure the variables Error, Trend, Season and Dampening in the tool and specify the time period to forecast. The model will be created based on our configuration.

### Summary

ARIMA and ETS are the most commonly used methods for creating time series models. Watch out for our next blog where we talk in detail on implementing these models in Alteryx.

### Tool configuration help:

Read more about similar Self Service BI topic here and learn more about Visual BI Solutions Microsoft Power BI offerings here.

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