Conditional Time Series Forecasting with Convolutional Neural Networks
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.
Code (3)
Tasks
Time SeriesTime Series AnalysisTime Series ForecastingMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Dilated Convolutional Neural Networks for Time Series Forecasting
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 ac…
Time SeriesTime Series AnalysisTime Series ForecastingA Generic Approach to Integrating Time into Spatial-Temporal Forecasting via Conditional Neural Fields
Self-awareness is the key capability of autonomous systems, e.g., autonomous driving network, which relies on highly efficient time series forecasting algorithm to enable the system to reason about the future state of th…
Autonomous DrivingOpen-Ended Question AnsweringTime SeriesTime Series ForecastingTransferable Time-Series Forecasting under Causal Conditional Shift
This paper focuses on the problem of semi-supervised domain adaptation for time-series forecasting, which is underexplored in literatures, despite being often encountered in practice. Existing methods on time-series doma…
Domain AdaptationSemi-supervised Domain AdaptationTime SeriesTime Series Analysis+1DistDF: Time-Series Forecasting Needs Joint-Distribution Wasserstein Alignment
Training time-series forecasting models requires aligning the conditional distribution of model forecasts with that of the label sequence. The standard direct forecast (DF) approach resorts to minimizing the conditional …
Flow Matching with Gaussian Process Priors for Probabilistic Time Series Forecasting
Recent advancements in generative modeling, particularly diffusion models, have opened new directions for time series modeling, achieving state-of-the-art performance in forecasting and synthesis. However, the reliance o…
Gaussian ProcessesProbabilistic Time Series ForecastingTime SeriesTime Series Forecasting