Merge pull request #79158 from nacharya1/patch-21

added time-series forecast section

Author: PRMerger9

articles/machine-learning/service/concept-automated-ml.md

@@ -9,7 +9,7 @@ ms.topic: conceptual
 ms.reviewer: jmartens
 author: nacharya1
 ms.author: nilesha
-ms.date: 05/21/2019
+ms.date: 06/10/2019
 ms.custom: seodec18
 
 ---
@@ -59,6 +59,20 @@ While model building is automated, you can also [learn how important or relevant
 
 <a name="preprocess"></a>
 
+## Time-Series Forecasting
+Building forecasts is an integral part of any business, whether it’s revenue, inventory, sales, or customer demand. 
+Automated ml uses a number of combined techniques and approaches to recommend a high quality time-series forecast. Time-series experiments in Automated ml are treated as a multivariate regression problem. Past time series values are “pivoted” to become additional dimensions for the regressor together with other predictors. 
+
+This approach, unlike classical time series methods, has an advantage of naturally incorporating multiple contextual variables and their relationship to one another during training. In real-world forecasting applications, multiple factors can influence a forecast. For example, when forecasting sales, interactions of historical trends, exchange rate and price all jointly drive the sales outcome. A further benefit is that all recent innovations in regression models apply immediately to forecasting.
+
+How far into the future the forecast should extend (the forecast horizon) is part of the basic forecast specification. Setting the required parameter of `max_horizon` in the experiment defines how many unit periods (based on the time interval of your training data, e.g. monthly, weekly the forecaster should predict out. 
+
+Automated ML learns a single, but often internally branched model for all items in the dataset and prediction horizons. More data is thus available to estimate model parameters and generalization to unseen series becomes possible. 
+
+Features extracted from the training data play a critical role. Automated ML performs standard pre-processing steps and generates additional time-series features (e.g. year, month, day of week etc.) to capture seasonal effects and maximize predictive accuracy. 
+
+If appropriate for your scenario, you can direct Automated ML to create lags (`target_lags`) or rolling-window aggregation of data (`target_rolling_window_size`) from your target’s (`y_value`) past values. 
+
 ## Preprocessing
 
 In every automated machine learning experiment, your data is preprocessed using the default methods and optionally through advanced preprocessing.