Time series forecasting is typically discussed where only a onestep prediction is required.
What about when you need to predict multiple time steps into the future?
Predicting multiple time steps into the future is called multistep time series forecasting. There are four main strategies that you can use for multistep forecasting.
In this post, you will discover the four main strategies for multistep time series forecasting.
After reading this post, you will know:
 The difference between onestep and multiplestep time series forecasts.
 The traditional direct and recursive strategies for multistep forecasting.
 The newer directrecursive hybrid and multiple output strategies for multistep forecasting.
Let’s get started.
MultiStep Forecasting
Generally, time series forecasting describes predicting the observation at the next time step.
This is called a onestep forecast, as only one time step is to be predicted.
There are some time series problems where multiple time steps must be predicted. Contrasted to the onestep forecast, these are called multiplestep or multistep time series forecasting problems.
For example, given the observed temperature over the last 7 days:

Time, Temperature 1, 56 2, 50 3, 59 4, 63 5, 52 6, 60 7, 55 
A singlestep forecast would require a forecast at time step 8 only.
A multistep may require a forecast for the next two days, as follows:

Time, Temperature 8, ? 9, ? 
There are at least four commonly used strategies for making multistep forecasts.
They are:
 Direct Multistep Forecast Strategy.
 Recursive Multistep Forecast Strategy.
 DirectRecursive Hybrid Multistep Forecast Strategies.
 Multiple Output Forecast Strategy.
Let’s take a closer look at each method in turn.
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1. Direct Multistep Forecast Strategy
The direct method involves developing a separate model for each forecast time step.
In the case of predicting the temperature for the next two days, we would develop a model for predicting the temperature on day 1 and a separate model for predicting the temperature on day 2.
For example:

prediction(t+1) = model1(obs(t1), obs(t2), …, obs(tn)) prediction(t+2) = model1(obs(t2), obs(t3), …, obs(tn)) 
Having one model for each time step is an added computational and maintenance burden, especially as the number of time steps to be forecasted increases beyond the trivial.
Because separate models are used, it means that there is no opportunity to model the dependencies between the predictions, such as the prediction on day 2 being dependent on the prediction in day 1, as is often the case in time series.
2. Recursive Multistep Forecast
The recursive strategy involves using a onestep model multiple times where the prediction for the prior time step is used as an input for making a prediction on the following time step.
In the case of predicting the temperature for the next two days, we would develop a onestep forecasting model. This model would then be used to predict day 1, then this prediction would be used as an observation input in order to predict day 2.
For example:

prediction(t+1) = model(obs(t1), obs(t2), …, obs(tn)) prediction(t+2) = model(prediction(t1), obs(t2), …, obs(tn)) 
Because predictions are used in place of observations, the recursive strategy allows prediction errors to accumulate such that performance can quickly degrade as the prediction time horizon increases.
3. DirectRecursive Hybrid Strategies
The direct and recursive strategies can be combined to offer the benefits of both methods.
For example, a separate model can be constructed for each time step to be predicted, but each model may use the predictions made by models at prior time steps as input values.
We can see how this might work for predicting the temperature for the next two days, where two models are used, but the output from the first model is used as an input for the second model.
For example:

prediction(t+1) = model1(obs(t1), obs(t2), …, obs(tn)) prediction(t+2) = model2(prediction(t1), obs(t2), …, obs(tn)) 
Combining the recursive and direct strategies can help to overcome the limitations of each.
4. Multiple Output Strategy
The multiple output strategy involves developing one model that is capable of predicting the entire forecast sequence in a oneshot manner.
In the case of predicting the temperature for the next two days, we would develop one model and use it to predict the next two days as one operation.
For example:

prediction(t+1), prediction(t+2) = model(obs(t1), obs(t2), …, obs(tn)) 
Multiple output models are more complex as they can learn the dependence structure between inputs and outputs as well as between outputs.
Being more complex may mean that they are slower to train and require more data to avoid overfitting the problem.
Further Reading
See the resources below for further reading on multistep forecasts.
Summary
In this post, you discovered strategies that you can use to make multiplestep time series forecasts.
Specifically, you learned:
 How to train multiple parallel models in the direct strategy or reuse a onestep model in the recursive strategy.
 How to combine the best parts of the direct and recursive strategies in the hybrid strategy.
 How to predict the entire forecast sequence in a oneshot manner using the multiple output strategy.
Do you have any questions about multistep time series forecasts, or about this post? Ask your questions in the comments below and I will do my best to answer.