-
-
**Description:**
Predicting future traffic flow, which will aid in traffic management and planning. The goal is to build a model that can accurately forecast traffic flow based on historical data a…
-
## About
At [^1][^2], we shared a few notes about time series anomaly detection, and forecasting/prediction. Other than using traditional statistics-based time series forecasting methods like [Holt…
amotl updated
1 month ago
-
This issue collects a wishlist of de-novo implementations of torch based models.
Anyone can suggest models to implement, we will have to prioritize along a impact/cost analysis.
FYI @fnhirwa.
C…
-
-
**Is your feature request related to a problem? Please describe.**
In deep learning-based time series forecasting, a model is often trained on multiple time series (e.g. Hourly Subset of the M4 Compe…
-
## Current Behavior
The integration [test](https://github.com/aimclub/FEDOT/blob/d2a6785b70ec339676ef2874a727a663098875a5/test/integration/real_applications/test_examples.py#L86) fails quite often, p…
-
As far as I know, currently the forecasting benchmarking (as well as evaluate) is not working for global setting. I.e., fitting a forecasting model on time series 1:n and evaluating it on n+1:m with n
-
As a demonstration of the concept for time-series benchmarking #494, more autoML frameworks capable of predicting time series should be provided with benchmarking support.
This issue is dedicated …
-
## Goal
We should have a consistent interface for creating forecasts from models.
## Context
For implementation inspiration, we can look at the [NumPyro Time Series Forecasting](https://num.p…