# Batch predictions for TTS and LSTM models
URL: https://docs.datarobot.com/en/docs/classic-ui/modeling/time/ts-tts-lstm-batch-pred.html

> Make batch predictions for Traditional Time Series (TTS) and Long Short-Term Memory (LSTM) models.

# Batch predictions for TTS and LSTM models

Traditional Time Series (TTS) and Long Short-Term Memory (LSTM) models—sequence models that use autoregressive (AR) and moving average (MA) methods—are common in time series forecasting. Both AR and MA models typically require a complete history of past forecasts to make predictions. In contrast, other time series models only require a single row after feature derivation to make predictions.

> [!NOTE] Note
> Time series Autopilot doesn't include TTS or LSTM model blueprints; however, you can access the model blueprints in the [model Repository](https://docs.datarobot.com/en/docs/classic-ui/modeling/build-models/build-basic/repository.html).

To allow batch predictions with TTS and LSTM models:

- Batch predictions accept historical data up to the maximum batch size (equal to 50MB or approximately a million rows of historical data).
- TTS models allow refitting on an incomplete history (if the complete history isn't provided).

> [!WARNING] Warning
> If you don't provide sufficient historical data at prediction time, you could encounter prediction inconsistencies. For more information on maintaining accuracy in TTS and LSTM models, see the [prediction accuracy considerations](https://docs.datarobot.com/en/docs/classic-ui/modeling/time/ts-tts-lstm-batch-pred.html#prediction-accuracy-considerations) below.

To make predictions with a deployed TTS or LSTM model,  access the [Predictions > Make predictions](https://docs.datarobot.com/en/docs/classic-ui/predictions/batch/batch-dep/batch-pred.html) and [Predictions > Job definitions](https://docs.datarobot.com/en/docs/classic-ui/predictions/batch/batch-dep/batch-pred-jobs.html) tabs.

## Prediction accuracy considerations

Calculating the percentage difference in RMSE between "full history" and "incomplete history" predictions measures the impact of using an incomplete feature derivation window history when making batch predictions with sequence models. Based on testing, DataRobot recommends applying the following guidelines to maintain prediction accuracy:

- ARIMA and ETS: These models use a smooth method (based on Kalman filtering), which does not change model parameters and uses the original model if refitting fails. To maintain accuracy, provide at least 20 points of historical data. It is particularly important to provide sufficient historical data to effectively smooth the new data with existing parameters when the FDW is small and the forecast isn't seasonal.
- TBATS and PROPHET: These models use a warm-start method, which uses the existing model parameters as an initial "guess" and completes the refit with more data. The model parameters can change, and the accuracy results are less consistent. To maintain accuracy, provide at least 40 points of historical data.
- LSTM: There are two groups of LSTM models.

---
