16 research outputs found
Correlated Time Series Self-Supervised Representation Learning via Spatiotemporal Bootstrapping
Correlated time series analysis plays an important role in many real-world
industries. Learning an efficient representation of this large-scale data for
further downstream tasks is necessary but challenging. In this paper, we
propose a time-step-level representation learning framework for individual
instances via bootstrapped spatiotemporal representation prediction. We
evaluated the effectiveness and flexibility of our representation learning
framework on correlated time series forecasting and cold-start transferring the
forecasting model to new instances with limited data. A linear regression model
trained on top of the learned representations demonstrates our model performs
best in most cases. Especially compared to representation learning models, we
reduce the RMSE, MAE, and MAPE by 37%, 49%, and 48% on the PeMS-BAY dataset,
respectively. Furthermore, in real-world metro passenger flow data, our
framework demonstrates the ability to transfer to infer future information of
new cold-start instances, with gains of 15%, 19%, and 18%. The source code will
be released under the GitHub
https://github.com/bonaldli/Spatiotemporal-TS-Representation-LearningComment: Accepted to IEEE CASE 202
Path cost distribution estimation using trajectory data
With the growing volumes of vehicle trajectory data, it becomes increasingly possible to capture time-varying and uncertain travel costs in a road network, including travel time and fuel consumption. The current paradigm represents a road network as a weighted graph; it blasts trajectories into small fragments that fit the under-lying edges to assign weights to edges; and it then applies a routing algorithm to the resulting graph. We propose a new paradigm, the
hybrid graph
, that targets more accurate and more efficient path cost distribution estimation. The new paradigm avoids blasting trajectories into small fragments and instead assigns weights to paths rather than simply to the edges.
We show how to compute path weights using trajectory data while taking into account the travel cost dependencies among the edges in the paths. Given a departure time and a query path, we show how to select an optimal set of weights with associated paths that cover the query path and such that the weights enable the most accurate joint cost distribution estimation for the query path. The cost distribution of the query path is then computed accurately using the joint distribution. Finally, we show how the resulting method for computing cost distributions of paths can be integrated into existing routing algorithms. Empirical studies with substantial trajectory data from two different cities offer insight into the design properties of the proposed method and confirm that the method is effective in real-world settings.</jats:p