A Spatial-Temporal Decomposition Based Deep Neural Network for Time Series Forecasting

by   Reza Asadi, et al.

Spatial time series forecasting problems arise in a broad range of applications, such as environmental and transportation problems. These problems are challenging because of the existence of specific spatial, short-term and long-term patterns, and the curse of dimensionality. In this paper, we propose a deep neural network framework for large-scale spatial time series forecasting problems. We explicitly designed the neural network architecture for capturing various types of patterns. In preprocessing, a time series decomposition method is applied to separately feed short-term, long-term and spatial patterns into different components of a neural network. A fuzzy clustering method finds cluster of neighboring time series based on similarity of time series residuals; as they can be meaningful short-term patterns for spatial time series. In neural network architecture, each kernel of a multi-kernel convolution layer is applied to a cluster of time series to extract short-term features in neighboring areas. The output of convolution layer is concatenated by trends and followed by convolution-LSTM layer to capture long-term patterns in larger regional areas. To make a robust prediction when faced with missing data, an unsupervised pretrained denoising autoencoder reconstructs the output of the model in a fine-tuning step. The experimental results illustrate the model outperforms baseline and state of the art models in a traffic flow prediction dataset.


Pre-training Enhanced Spatial-temporal Graph Neural Network for Multivariate Time Series Forecasting

Multivariate Time Series (MTS) forecasting plays a vital role in a wide ...

PCNN: Deep Convolutional Networks for Short-term Traffic Congestion Prediction

Traffic problems have seriously affected people's life quality and urban...

MPPN: Multi-Resolution Periodic Pattern Network For Long-Term Time Series Forecasting

Long-term time series forecasting plays an important role in various rea...

On the predictability of Rainfall in Kerala- An application of ABF Neural Network

Rainfall in Kerala State, the southern part of Indian Peninsula in parti...

An autoencoder wavelet based deep neural network with attention mechanism for multistep prediction of plant growth

Multi-step prediction is considered of major significance for time serie...

Time Series Forecasting via Semi-Asymmetric Convolutional Architecture with Global Atrous Sliding Window

The proposed method in this paper is designed to address the problem of ...

Pattern Similarity-based Machine Learning Methods for Mid-term Load Forecasting: A Comparative Study

Pattern similarity-based methods are widely used in classification and r...

Please sign up or login with your details

Forgot password? Click here to reset