  Forecasting multivariate time series data, such as prediction of electricity
consumption, solar power production, and polyphonic piano pieces, has numerous
valuable applications. However, complex and non-linear interdependencies
between time steps and series complicate the task. To obtain accurate
prediction, it is crucial to model long-term dependency in time series data,
which can be achieved to some good extent by recurrent neural network (RNN)
with attention mechanism. Typical attention mechanism reviews the information
at each previous time step and selects the relevant information to help
generate the outputs, but it fails to capture the temporal patterns across
multiple time steps. In this paper, we propose to use a set of filters to
extract time-invariant temporal patterns, which is similar to transforming time
series data into its "frequency domain". Then we proposed a novel attention
mechanism to select relevant time series, and use its "frequency domain"
information for forecasting. We applied the proposed model on several
real-world tasks and achieved state-of-the-art performance in all of them with
only one exception.
