Time Series Forecasting
Safetensors
PyTorch
fracast
fracast-0
time-series-foundation-model
foundation-models
pretrained-models
pretrained
time-series
timeseries
forecasting
probabilistic-forecasting
cpu
quantization
w8
gift-eval
fev-bench
time-benchmark
boom
github
mimo-2-6-pro
deepseek-v4-1-flash
xiaomi-mimo
deepseek
arxiv
model-compression
browser-inference
Eval Results (legacy)
Remove leakage-affected FEV highlights
Browse filesKeep scenario highlights to clean GIFT-Eval and TIME slices.
README.md
CHANGED
|
@@ -298,12 +298,6 @@ Naive. Lower is better, and TIME ranks use the official 29-model table.
|
|
| 298 |
- **TIME, Manufacturing:** On `Smart_Manufacturing/H`, medium and long
|
| 299 |
horizons rank 8th of 29 by MASE, while CRPS ranks range from 8th to 10th
|
| 300 |
across all three horizons.
|
| 301 |
-
- **FEV-Bench, Multivariate event series:** Each of the 15 BoomLet tasks
|
| 302 |
-
improves on Seasonal Naive across SQL, MASE, WQL, and WAPE. Their
|
| 303 |
-
geometric-mean SQL and WQL ratios are 0.403 and 0.419.
|
| 304 |
-
- **FEV-Bench, Weather uncertainty:** Three Jena weather tasks reduce WQL to
|
| 305 |
-
0.261 of Seasonal Naive and improve every reported metric.
|
| 306 |
-
|
| 307 |
These are scenario slices, not separate aggregate rankings. The benchmark
|
| 308 |
disclosures above still apply.
|
| 309 |
|
|
|
|
| 298 |
- **TIME, Manufacturing:** On `Smart_Manufacturing/H`, medium and long
|
| 299 |
horizons rank 8th of 29 by MASE, while CRPS ranks range from 8th to 10th
|
| 300 |
across all three horizons.
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 301 |
These are scenario slices, not separate aggregate rankings. The benchmark
|
| 302 |
disclosures above still apply.
|
| 303 |
|