Table 1.

The long-term forecasting error comparison of different methods using CATS data.


#### Table 2.

Meta-parameters of DBN used for the CATS data (block 1).

Figure 8.

Change of the learning error during fine-tuning (CATS data [1–980]).


Training Deep Neural Networks with Reinforcement Learning for Time Series Forecasting DOI: http://dx.doi.org/10.5772/intechopen.85457

• Sunspot number: Monthly averages of sunspot numbers from A.D. 1749 to the present 3078 values

The prediction results of these three datasets are shown in Figure 9. Short-term prediction error is shown in Table 3. DBN with the SGA learning method showed its priority in all cases.

The efficiency of random search to find the optimal meta-parameters, i.e., the structure of RBM and MLP, learning rates, discount factor, etc. which are explained in Section 2.5 is shown in Figure 10 in the case of DBN with SGA learning algorithm. The random search results are shown in Table 4.

We also used seven types of natural phenomenon time series data of TSDL [18]. The data to be predicted was chosen based on [19] which are named as Lynx, Sunspots, River flow, Vehicles, RGNP, Wine, and Airline. The short-term (oneahead) prediction results are shown in Figure 11 and Table 5.

From Table 5, it can be confirmed that SGA showed its priority to BP except the cases of Vehicles and Wine. From Table 6, an interesting result of random search for meta-parameter showed that the structures of DBN for different datasets were different, not only the number of units on each layer but also the number of RBMs. In the case of SGA learning method, the number of layer for Sunspots, River flow, and Wine were more than DBN using BP learning.
