We lifted 13 functions out of this paper's own repositories and ran 11 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 |
|---|---|---|
| phschiele/armacell_paper | canonical | 8 of 10 |
| phschiele/armacell | canonical | 3 of 3 |
| Function | Status | Where it lives |
|---|---|---|
| create_shifted_frames | Ran | phschiele/armacell_paper/benchmarks/common.py code served (permissive licence) · get_code("9bc27a1edb4337c6") |
| get_mae | Ran | phschiele/armacell_paper/models/helpers.py code served (permissive licence) · get_code("60dbccf5b79f6dde") |
| get_mse | Ran | phschiele/armacell_paper/models/helpers.py code served (permissive licence) · get_code("e3192303e3a9ebfb") |
| get_rmse | Ran | phschiele/armacell_paper/models/helpers.py code served (permissive licence) · get_code("363fdb90133a1244") |
| get_varma | Ran | phschiele/armacell_paper/models/models.py code served (permissive licence) · get_code("68b2ccab5e7ad304") |
| prepare_arma_input | Ran | phschiele/armacell/armacell/helpers.py code served (permissive licence) · get_code("16cbd75e75aac828") |
| prepare_arma_input | Ran | phschiele/armacell_paper/arma_cell/helpers.py code served (permissive licence) · get_code("8190c6aa9a92a9c5") |
| restore_arma_parameters | Ran | phschiele/armacell/armacell/helpers.py code served (permissive licence) · get_code("8b0d52a3aa809568") |
| restore_arma_parameters | Ran | phschiele/armacell_paper/arma_cell/helpers.py code served (permissive licence) · get_code("8071c9616f2ce570") |
| simulate_arma_process | Ran | phschiele/armacell/armacell/helpers.py code served (permissive licence) · get_code("d3949134e1069510") |
| simulate_arma_process | Ran | phschiele/armacell_paper/arma_cell/helpers.py code served (permissive licence) · get_code("a665bb02e0d653cb") |
| generate_movies | Not yet run | phschiele/armacell_paper/benchmarks/moving_squares.py code served (permissive licence) · get_code("f9508098e7091d77") |
| get_convlstm | Not yet run | phschiele/armacell_paper/models/models.py code served (permissive licence) · get_code("f86ddb0f233f60b1") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
The autoregressive moving average (ARMA) model is a classical, and arguably one of the most studied approaches to model time series data. It has compelling theoretical properties and is widely used among practitioners. More recent deep learning approaches popularize recurrent neural networks (RNNs) and, in particular, Long Short-Term Memory (LSTM) cells that have become one of the best performing and most common building blocks in neural time series modeling. While advantageous for time series data or sequences with long-term effects, complex RNN cells are not always a must and can sometimes even be inferior to simpler recurrent approaches. In this work, we introduce the ARMA cell, a simpler, modular, and effective approach for time series modeling in neural networks. This cell can be used in any neural network architecture where recurrent structures are present and naturally handles multivariate time series using vector autoregression. We also introduce the ConvARMA cell as a natural successor for spatially-correlated time series. Our experiments show that the proposed methodology is competitive with popular alternatives in terms of performance while being more robust and compelling due to its simplicity
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2208.14919")
get_code_for_paper("2208.14919")
have("2208.14919")
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