Model card
ES-RNN.
Uber Technologiesopen-sourceUnknown paramsRNN
Time series forecasting model
§ 01 · Benchmarks
Every benchmark ES-RNN has a recorded score for.
| # | Benchmark | Area · Task | Metric | Value | Rank | Date | Source |
|---|---|---|---|---|---|---|---|
| 01 | M4 Competition | Time Series · Time Series Forecasting | mase | 1.5% | #12 | 2020-01-01 | source ↗ |
| 02 | M4 Competition | Time Series · Time Series Forecasting | owa | 0.8% | #12 | 2020-01-01 | source ↗ |
| 03 | M4 Competition | Time Series · Time Series Forecasting | smapi | 11.4% | #13 | 2020-01-01 | source ↗ |
Rank column shows this model’s position vs all other models scored on the same benchmark + metric (competitors after the slash). #1 in red means current SOTA. Sorted by rank, then newest result.
§ 03 · Papers
1 paper with results for ES-RNN.
- 2020-01-01· Time Series· 3 results
A hybrid method of exponential smoothing and recurrent neural networks for time series forecasting
Slawek Smyl
§ 05 · Sources & freshness
Where these numbers come from.
M4 Competition (Smyl 2018)
3
results
3 of 3 rows marked verified.