DeepAR

DeepAR is a forecasting algorithm in AWS SageMaker that uses a recurrent neural network (RNN) to model time series data.

How It Works

DeepAR works by training an RNN on a large number of related time series. The RNN learns to recognize patterns across these series and uses this knowledge to make forecasts.

Benefits

• Handling of Multiple Time Series: DeepAR can handle multiple related time series simultaneously.
• Probabilistic Forecasts: DeepAR provides not just point forecasts but also uncertainty estimates.

Limitations

• Requires Large Datasets: DeepAR typically requires large amounts of data to train effectively.

Features

• Recurrent Neural Network: DeepAR uses an RNN to model time series data.
• Exogenous Variables: DeepAR can incorporate additional variables that may influence the time series.

Use Cases

• Demand Forecasting: DeepAR can be used to forecast demand for products or services.
• Energy Usage Forecasting: DeepAR can be used to forecast energy usage based on historical data.

ARIMA

ARIMA (AutoRegressive Integrated Moving Average) is a traditional forecasting method that models time series data based on its own past values and error terms.

How It Works

ARIMA works by using past data points and errors to predict future values. It involves identifying the order of differencing (I), the order of the autoregressive term (AR), and the order of the moving average term (MA).

Benefits