We lifted 3 functions out of this paper's own repositories and ran 3 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| rajatsen91/deepglo | canonical | 2 of 2 |
| copy not recorded | — | 1 of 1 |
| Function | Status | Where it lives |
|---|---|---|
| bool2str | Ran | rajatsen91/deepglo/run_scripts/run_electricity.py pointer only (licence: NOASSERTION) · get_code("33b1599b41564b9c") |
| get_model | Ran | rajatsen91/deepglo/DeepGLO/DeepGLO.py pointer only (licence: NOASSERTION) · get_code("43fdc90d40db6b9a") |
| str2bool | Ran | this paper's copy was not recorded; identical code first harvested from sajadn/Exemplar-VAE pointer only · get_code("7c508037b40522af") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Forecasting high-dimensional time series plays a crucial role in many applications such as demand forecasting and financial predictions. Modern datasets can have millions of correlated time-series that evolve together, i.e they are extremely high dimensional (one dimension for each individual time-series). There is a need for exploiting global patterns and coupling them with local calibration for better prediction. However, most recent deep learning approaches in the literature are one-dimensional, i.e, even though they are trained on the whole dataset, during prediction, the future forecast for a single dimension mainly depends on past values from the same dimension. In this paper, we seek to correct this deficiency and propose DeepGLO, a deep forecasting model which thinks globally and acts locally. In particular, DeepGLO is a hybrid model that combines a global matrix factorization model regularized by a temporal convolution network, along with another temporal network that can capture local properties of each time-series and associated covariates. Our model can be trained effectively on high-dimensional but diverse time series, where different time series can have vastly different scales, without a priori normalization or rescaling. Empirical results demonstrate that DeepGLO can outperform state-of-the-art approaches; for example, we see more than 25% improvement in WAPE over other methods on a public dataset that contains more than 100K-dimensional time series.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("1905.03806")
get_code_for_paper("1905.03806")
have("1905.03806")
Connect an agent — have() is free.