Comments (8)
Is there a requirement for input and output step size?
from basicts.
Is there a requirement for input and output step size?
Yes, here are the steps:
- Generate samples:
python scripts/data_preparation/${DATASET_NAME}/generate_training_data.py --history_seq_len 12 future_seq_len 1
- Change the
CFG.DATASET_OUTPUT_LEN
andCFG.DATASET_INPUT_LEN
in the config files. - Change training parameters (optional). For example, for single step forecasting, the curriculum learning is no longer useful, and you can just delete the
CFG.TRAIN.CL
dict. - Change the model parameters (optional) The parameters of the model need to read its original paper to determine how to modify it.
from basicts.
Modified by the above method, but the prediction accuracy is not ideal.Not even as good as multi-step prediction.
from basicts.
And just delete the dict.CFG.TRAIN.CL, but an error occurred in the program.I modify CFG.TRAIN.CL.PREDICTION_LENGTH = 1,It can run, but with poor accuracy
from basicts.
And just delete the dict.CFG.TRAIN.CL, but an error occurred in the program.
The reason is that the DGCRN model requires the implementation of CL (for details, please refer to its paper). Please use the following configuration:
...
CFG.DATASET_INPUT_LEN = 12
CFG.DATASET_OUTPUT_LEN = 1 # *** change the DATASET_OUTPUT_LEN from 12 to 1 ***
CFG.MODEL.PARAM = {
"gcn_depth": 2,
"num_nodes": 207,
"predefined_A": [torch.Tensor(_) for _ in adj_mx],
"dropout": 0.3,
"subgraph_size": 20,
"node_dim": 40,
"middle_dim": 2,
"seq_length": 1, # *** change the seq_length from 12 to 1 ***
"in_dim": 2,
"list_weight": [0.05, 0.95, 0.95],
"tanhalpha": 3,
"cl_decay_steps": 4000,
"rnn_size": 64,
"hyperGNN_dim": 16
}
...
## curriculum learning
CFG.TRAIN.CL = EasyDict()
CFG.TRAIN.CL.WARM_EPOCHS = 0
CFG.TRAIN.CL.CL_EPOCHS = 1
CFG.TRAIN.CL.PREDICTION_LENGTH = 1
...
# ***add the evaluation horizon configs***
CFG.EVAL = EasyDict()
CFG.EVAL.HORIZONS = [1]
from basicts.
By the way, kindly note that the acc of single-step prediction is not necessarily better than the acc of multi-step prediction at Horizon 1.
from basicts.
thanks
from basicts.
I want to ask you the reason that the acc of single-step prediction is not necessarily better than the acc of multi-step prediction at Horizon 1. And Why the acc of multi-step prediction at Horizon 1 does not decline with iteration.
from basicts.
Related Issues (20)
- Cannot run inference.py on VMWare 2016 workstation. HOT 20
- 复现代码中的参数设置问题 HOT 3
- 如何修改数据集预测的时间长度,我修改了生成数据集的参数和模型的参数后存在如下问题 HOT 1
- some issue about model performance HOT 1
- thanks HOT 1
- AGCRN,PEMS08数据集的配置问题 HOT 1
- 请问如何使用inference程序,需要改哪些部分 HOT 15
- Add contributors. HOT 16
- 请问如何修改学习率衰减器 HOT 1
- 修改数据归一化方式。 HOT 1
- MS (multivariate -> univariate)? HOT 2
- 关于数据输入和输出特征个数的问题? HOT 2
- dataloader的droplast问题 HOT 2
- STGCN 模型不能跑第二次 HOT 1
- implementation of spatial indistinguishability HOT 1
- 能否支持early stop? HOT 1
- 您好,请教一下模型训练好后如何获取预测值与真实值的数据 HOT 5
- 怎么快速的修改输入输出长度,完成不同实验? HOT 1
- 关于训练速度 HOT 3
- 如何多卡训练 HOT 1
Recommend Projects
-
React
A declarative, efficient, and flexible JavaScript library for building user interfaces.
-
Vue.js
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
-
Typescript
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
-
TensorFlow
An Open Source Machine Learning Framework for Everyone
-
Django
The Web framework for perfectionists with deadlines.
-
Laravel
A PHP framework for web artisans
-
D3
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
-
Recommend Topics
-
javascript
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
-
web
Some thing interesting about web. New door for the world.
-
server
A server is a program made to process requests and deliver data to clients.
-
Machine learning
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
-
Visualization
Some thing interesting about visualization, use data art
-
Game
Some thing interesting about game, make everyone happy.
Recommend Org
-
Facebook
We are working to build community through open source technology. NB: members must have two-factor auth.
-
Microsoft
Open source projects and samples from Microsoft.
-
Google
Google ❤️ Open Source for everyone.
-
Alibaba
Alibaba Open Source for everyone
-
D3
Data-Driven Documents codes.
-
Tencent
China tencent open source team.
from basicts.