Forecasting
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.
Select date/time column with "Date / Time Column" dropdown. This columns is the time axis along which forecasting is performed.
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
Holiday Column - A categorical column that indicates type of holiday for the day of the observation. When logical column is selected, TRUE means it is a holiday and FALSE means it is not.
Data Frame for Holidays - Holiday definition data frame
Use Built-in Country Holiday Data - By setting this option to Yes, you can specify list of country codes whose holiday data should be considered in making forecast.
Country for Holiday Data - Specify list of country codes here. For supported countries, take a look at this table.
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.
