Abstract
In this chapter, we consider three different real-world datasets, which contain real-valued time series of measurements of electricity and telephonic activity load. For each dataset, we set up a short-term load forecast problem of 24 hours ahead prediction. Two of the datasets under analysis include time series of measurements of exogenous variables, which are used to provide additional context to the network and thus to improve the accuracy of the prediction. For each dataset, we perform an analysis to study the nature of the time series, in terms of its correlation properties, seasonal patterns, correlation with the exogenous time series, and nature of the variance. According to the result of our analysis, we select a suitable preprocessing strategy before feeding the data into the recurrent neural networks. As shown in the following, the forecast accuracy in a prediction problem can be considerably improved by proper preprocessing of data (Zhang and Qi 2005).
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Bianchi, F.M., Maiorino, E., Kampffmeyer, M.C., Rizzi, A., Jenssen, R. (2017). Real-World Load Time Series. In: Recurrent Neural Networks for Short-Term Load Forecasting. SpringerBriefs in Computer Science. Springer, Cham. https://doi.org/10.1007/978-3-319-70338-1_6
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DOI: https://doi.org/10.1007/978-3-319-70338-1_6
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