Conditional Time Series Forecasting with Convolutional Neural Networks (1703.04691v5)

Abstract: We present a method for conditional time series forecasting based on an adaptation of the recent deep convolutional WaveNet architecture. The proposed network contains stacks of dilated convolutions that allow it to access a broad range of history when forecasting, a ReLU activation function and conditioning is performed by applying multiple convolutional filters in parallel to separate time series which allows for the fast processing of data and the exploitation of the correlation structure between the multivariate time series. We test and analyze the performance of the convolutional network both unconditionally as well as conditionally for financial time series forecasting using the S&P500, the volatility index, the CBOE interest rate and several exchange rates and extensively compare it to the performance of the well-known autoregressive model and a long-short term memory network. We show that a convolutional network is well-suited for regression-type problems and is able to effectively learn dependencies in and between the series without the need for long historical time series, is a time-efficient and easy to implement alternative to recurrent-type networks and tends to outperform linear and recurrent models.

Conditional Time Series Forecasting with Convolutional Neural Networks

The paper "Conditional Time Series Forecasting with Convolutional Neural Networks" presents an adaptation of the WaveNet architecture, exploring its application in financial time series forecasting. The work focuses on leveraging convolutional neural networks (CNNs) for this purpose, highlighting their ability to efficiently capture both linear and non-linear dependencies in multivariate time series data.

Methodology

The proposed methodology involves utilizing dilated convolutions within the WaveNet framework to access a broad temporal history. This design enables the network to accommodate long-term dependencies inherent in time series data. By employing ReLU activation functions and conducting conditioning through parallel convolutional filters, the authors aim to process multivariate financial data efficiently.

The model is specifically tailored for multivariate financial time series where conditional forecasting is key. The conditional approach takes advantage of multiple correlated time series to enhance the forecasting accuracy. Here, the network is conditioned on related series data, allowing it to exploit correlations effectively.

Experimental Evaluation

The experimental evaluation involves a comprehensive performance testing regime comparing the proposed network against traditional autoregressive models and LSTM networks. The paper encompasses various financial datasets, including the S&P500 index, VIX, CBOE interest rates, and several exchange rates.

Numerical results demonstrate the CNN's superiority, with the model often outperforming both linear autoregressive and recurrent models like the LSTM. The network showcases strong performance in learning dependencies without requiring extensive historical data, exhibiting time efficiency and ease of implementation.

Implications and Future Directions

The implications of using CNNs for time series forecasting are significant. The method offers a promising alternative to existing recurrent networks, providing a simplified yet effective mechanism for handling noisy, complex datasets like those in finance. The research indicates potential broader applications of CNNs beyond traditional domains, supporting their adaptation for regression settings in time series.

Regarding future developments, the paper suggests exploring CNN enhancements, such as using adaptive filters or combining models for increased non-linearity capturing without overfitting. Additional speed-up techniques, such as frequency-domain strategies, could facilitate handling even larger datasets efficiently.

Overall, this paper contributes to advancing the utility of convolutional neural networks in time series forecasting, indicating a strong foundation for future explorations in AI-driven financial predictions and beyond.