PaddleX Time Series Task Model Configuration File Parameters Explanation¶

Global¶

Parameter Name	Data Type	Description	Default Value
model	str	Specifies the model name	Model name specified in the YAML file
mode	str	Specifies the mode (check_dataset/train/evaluate/export/predict)	check_dataset
dataset_dir	str	Path to the dataset	Dataset path specified in the YAML file
device	str	Specifies the device to use	Device ID specified in the YAML file
output	str	Output path	"output"

Parameter Name	Data Type	Description	Default Value
convert.enable	bool	Whether to convert the dataset format; time series prediction, anomaly detection, and classification support data conversion from xlsx and xls formats	False
convert.src_dataset_type	str	The source dataset format to be converted	null
split.enable	bool	Whether to re-split the dataset	False
split.train_percent	int	Sets the percentage of the training set, an integer between 0-100, ensuring the sum with val_percent is 100;	null
split.val_percent	int	Sets the percentage of the validation set, an integer between 0-100, ensuring the sum with train_percent is 100;	null

Parameter Name	Data Type	Description	Default Value
epochs_iters	int	The number of times the model repeats learning the training data	Number of iterations specified in the YAML file
batch_size	int	Batch size	Batch size specified in the YAML file
learning_rate	float	Initial learning rate	Initial learning rate specified in the YAML file
time_col	str	Time column, set the column name of the time series dataset's time column based on your data.	Time column specified in the YAML file
freq	str or int	Frequency, set the time frequency based on your data, e.g., 1min, 5min, 1h.	Frequency specified in the YAML file

Parameter Name	Data Type	Description	Default Value
target_cols	str	Target variable column(s), set the column name(s) of the target variable(s) in the time series dataset, can be multiple, separated by commas	OT
input_len	int	For time series forecasting tasks, this parameter represents the length of historical time series input to the model; the input length should be considered in conjunction with the prediction length, generally, the larger the setting, the more historical information can be referenced, and the higher the model accuracy.	96
predict_len	int	The length of the future sequence that you want the model to predict; the prediction length should be considered in conjunction with the actual scenario, generally, the larger the setting, the longer the future sequence you want to predict, and the lower the model accuracy.	96
patience	int	Early stopping mechanism parameter, indicating how many times the model's performance on the validation set can be continuously unimproved before stopping training; a larger patience value generally results in longer training time.	10

Parameter Name	Data Type	Description	Default Value
input_len	int	For time series anomaly detection tasks, this parameter represents the length of the time series input to the model, which will slice the time series according to this length to predict whether there is an anomaly in this segment of the time series; the input length should be considered in conjunction with the actual scenario. For example, an input length of 96 indicates that you want to predict whether there are anomalies in 96 time points.	96
feature_cols	str	Feature variables indicating variables related to whether the device is abnormal, e.g., whether the device is abnormal may be related to the heat dissipation during its operation. Set the column name(s) of the feature variable(s) based on your data, can be multiple, separated by commas.	feature_0,feature_1
label_col	str	Represents the number indicating whether a time series point is abnormal, with 1 for abnormal points and 0 for normal points.	label

Parameter Name	Data Type	Description	Default Value
target_cols	str	Feature variable columns used for category discrimination. You need to set the column names of the target variables in the time series dataset based on your own data. It can be multiple, separated by commas.	dim_0,dim_1,dim_2
freq	str or int	Frequency, which needs to be set based on your own data. Examples of time frequencies include: 1min, 5min, 1h.	1
group_id	str	A group ID represents a time series sample. Time series sequences with the same ID constitute a sample. Set the column name of the specified group ID based on your own data, e.g., group_id.	group_id
static_cov_cols	str	Represents the category number column of the time series. The labels of the same sample are the same. Set the column name of the category based on your own data, e.g., label.	label

Parameter Name	Data Type	Description	Default Value
weight_path	str	Evaluation model path	Default local path from training output, when specified as None, indicates using official weights

Parameter Name	Data Type	Description	Default Value
weight_path	str	Dynamic graph weight path for exporting the model	Default local path from training output, when specified as None, indicates using official weights

Parameter Name	Data Type	Description	Default Value
batch_size	int	Prediction batch size	The prediction batch size specified in the YAML file
model_dir	str	Path to the prediction model	The default local inference model path produced by training. When specified as None, it indicates the use of official weights
input	str	Path to the prediction input	The prediction input path specified in the YAML file