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Time Series Forecasting

7 papers with code · Time Series

Time series forecasting is the task of predicting future values of a time series (as well as uncertainty bounds).

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Greatest papers with code

Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting

ICLR 2018 liyaguang/DCRNN

Spatiotemporal forecasting has various applications in neuroscience, climate and transportation domain. Traffic forecasting is one canonical example of such learning task.

MULTIVARIATE TIME SERIES FORECASTING SPATIO-TEMPORAL FORECASTING TRAFFIC PREDICTION

Modeling Long- and Short-Term Temporal Patterns with Deep Neural Networks

21 Mar 2017laiguokun/LSTNet

Multivariate time series forecasting is an important machine learning problem across many domains, including predictions of solar plant energy output, electricity consumption, and traffic jam situation. Temporal data arise in these real-world applications often involves a mixture of long-term and short-term patterns, for which traditional approaches such as Autoregressive models and Gaussian Process may fail.

MULTIVARIATE TIME SERIES FORECASTING TIME SERIES

Temporal Pattern Attention for Multivariate Time Series Forecasting

12 Sep 2018gantheory/TPA-LSTM

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.

MULTIVARIATE TIME SERIES FORECASTING TIME SERIES

Neural Decomposition of Time-Series Data for Effective Generalization

25 May 2017Sarunas-Girdenas/neural_decomposition

We present a neural network technique for the analysis and extrapolation of time-series data called Neural Decomposition (ND). Units with a sinusoidal activation function are used to perform a Fourier-like decomposition of training samples into a sum of sinusoids, augmented by units with nonperiodic activation functions to capture linear trends and other nonperiodic components.

TIME SERIES TIME SERIES FORECASTING

On-Line Learning of Linear Dynamical Systems: Exponential Forgetting in Kalman Filters

16 Sep 2018jmarecek/OnlineLDS

We show that the dependence of a prediction of Kalman filter on the past is decaying exponentially, whenever the process noise is non-degenerate. Based on this insight, we devise an on-line algorithm for improper learning of a linear dynamical system (LDS), which considers only a few most recent observations.

TIME SERIES TIME SERIES FORECASTING

MOrdReD: Memory-based Ordinal Regression Deep Neural Networks for Time Series Forecasting

26 Mar 2018bperezorozco/ordinal_tsf

In this work, we directly tackle this task with a novel, fully end-to-end deep learning method for time series forecasting. We showcase this key feature in a large-scale benchmark test with 45 datasets drawn from both, a wide range of real-world application domains, as well as a comprehensive list of synthetic maps.

TIME SERIES TIME SERIES FORECASTING

Conditional Time Series Forecasting with Convolutional Neural Networks

14 Mar 2017junwang23/deepdirtycodes

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.

TIME SERIES TIME SERIES FORECASTING