With Time Series Forecasting, you can calculate forecast data into the future based on the time series data frame from the past.

Input data should be a time series data frame. It should have following columns.

A date/time column with Date or POSIXct data.

A value column with numeric values that you want to forecast into the future.

Click "+" button and mouse over "Run Analytics ...", and select "Run Time Series Forecast" submenu to open "Run Time Series Forecast" dialog.

After "Run Time Series Forecast" dialog is opened, follow the steps below to calculate forecast data.

â€‹ 1. Select date/time column with "Date / Time Column" dropdown. This columns is the time axis along which forecasting is performed. 2. Select aggregation level from "Aggregation Level" dropdown. Input data is aggregated with this time unit. For example, if you select "Day" and there are multiple rows that falls within a same day, the values for those rows are aggregated to form single row for the date. As a result, this becomes the time unit for the resulting time series data frame. Aggregation level can be one of the following.

Day

Week

Month

Quarter

Year

Select value column from "Value Column" dropdown. This is the value of interest that you want to forecast.

Select aggregation function from "Aggregation Function" dropdown. This is how the multiple rows that falls under same time period (the one you specified with "Aggregation Level".) is aggregated. Its value can be one of the following.

sum

mean

count

median

max

min

first

last

mad - Median Absolute Deviation

sd - Standard Deviation

var - Variance

Specify the number of time period for which you want to forecast the value of interest in "Forecasting Time Period" text field.

(Optional) Specify additional parameters in "Parameters" section.

Capacity (Cap) Value or Data Frame - Possibly achievable maximum value. Can be numbers like market size, or some kind of theoretical limit.

Strength of Seasonality

Return Yearly Seasonality - Take yearly seasonality into account while forecasting.

Return Weekly Seasonality - Take weekly seasonality into account while forecasting.

Number of Potential Changepoints

Flexibility of Automatic Changepoint Selection

Potential Changepoints

Strength of Holiday Effect

Data Frame for Holidays - Holiday definition data frameâ€‹

MCMC Samples for Full Bayesian Inference

Width of Uncertainty Intervals

Number of Simulations for Uncertainty Intervals

Click "Run" button.

The output data comes with the following in addition to the input data.

Rows for forecasted data with future date values

Additional Columns about forecasted values

forecasted_value - The forecasted value.

forecasted_value_high - Upper bound of forecasted value

forecasted_value_low - Lower bound of forecasted value

trend

trend_high

trend_low

seasonal

seasonal_low

seasonal_high

yearly

yearly_high

yearly_low

weekly

weekly_high

weekly_low

cap_model

cap_forecast

Here is an example of line chart created from the output data. DEP_DELAY (blue line) is the input value column. Note that orange line for the forecasted_value goes into future beyond the end of the blue line.