CLASSIFICATION AND PREDICTION - UniversitÃ¤t Wien

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Peter Brezany Institut für Softwarewissenschaft, WS 2002 18 Neural Networks for Time Series (3) Slide 34 Notice that the time-series network is not limited to data from just a single time series. It can take multiple inputs. For instance, if we were trying to predict the value of the Swiss Franc to U.S. Dollar exchange rate, we might include other time-series information, such as the U.S. Dollar to Deutsch Mark exchange rate, the closing value of the stock exchange, etc. The number of historical units controls the length of the patterns that the network can recognize. For instance, keeping 10 historical units on a network predicting the closing price of a favorite stock will allow the network to recognize patterns that occur within two-week time periods. Neural Networks for Time Series (4) Actually, we can get the same effect of a time-series NN using a regular feed-forward, backpropagation network by modifying the input data. Say that we have the time-series, shown in the table below with 10 data elements and we are interested in two features: the day of the week and the closing price. Data Element Day-of-Week Closing Price Slide 35 1 1 2 2 3 3 4 4 5 5 6 1 7 2 8 3 9 4 10 5 $ 40.25 $ 41.00 $ 39.25 $ 39.75 $ 40.50 $ 40.50 $ 40.75 $ 41.25 $ 42.00 $ 4150
Peter Brezany Institut für Softwarewissenschaft, WS 2002 19 Neural Networks for Time Series (5) To create a time series with a time lag of three, we just add new features for the previous values - see the table below. This data can now be input into a feed-forward, backpropagation network without any special support for time series. Previous Previous-1 Data Element Day-of-Week Closing Price Closing Price Closing Price Slide 36 1 2 1 2 $ $ 40.25 41.00 $ 40.25 3 3 $ 39.25 $ 41.00 $ 40.25 4 5 6 7 8 9 4 5 1 2 3 4 $ $ $ $ $ $ 39.75 40.50 40.50 40.75 41.25 42.00 $ 39.25 $ 39.75 $ 40.50 $ 40.50 $ 40.75 $ 41.25 $ 41.00 $ 39.25 $ 39.75 $ 40.50 $ 40.50 $ 40.75 10 5 $ 41.50 $ 42.00 $ 41.25 Heuristics for Using Neural Networks Even with sophisticated NN packages, getting the best results from a NN takes some effort. 1. The number of units in the hidden layer - probably the biggest decision Slide 37 The more units, the more patterns the network can recognize. However, a very large layer might end up memorizing the training set instead of generalizing from it. In this case, more is not better. Fortunately, we can detect when a network is overtrained. If the network performs very well on the training set, but does much worse on the test set, then this is an indication that it has memorized the test set. How large should the hidden layer be? It should never be more than twice as large as the input layer. A good place to start is to make the hidden layer the same size as the input layer. If the network is overtraining, reduce the size of the layer. If it is not sufficiently accurate, increase its size. When using a network for classification, the network should start with one hidden unit for each class.
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