  Forecasting with multivariate time series, which aims to predict future
values given previous and current several univariate time series data, has been
studied for decades, with one example being ARIMA. Because it is difficult to
measure the extent to which noise is mixed with informative signals within
rapidly fluctuating financial time series data, designing a good predictive
model is not a simple task. Recently, many researchers have become interested
in recurrent neural networks and attention-based neural networks, applying them
in financial forecasting. There have been many attempts to utilize these
methods for the capturing of long-term temporal dependencies and to select more
important features in multivariate time series data in order to make accurate
predictions. In this paper, we propose a new prediction framework based on deep
neural networks and a trend filtering, which converts noisy time series data
into a piecewise linear fashion. We reveal that the predictive performance of
deep temporal neural networks improves when the training data is temporally
processed by a trend filtering. To verify the effect of our framework, three
deep temporal neural networks, state of the art models for predictions in time
series finance data, are used and compared with models that contain trend
filtering as an input feature. Extensive experiments on real-world multivariate
time series data show that the proposed method is effective and significantly
better than existing baseline methods.
