[LEADERG APP] XGBoost Regression Time Series

Language:

繁體中文

 

 

 

[Introduction]

 

Use XGBoost-Regression-Time-Series to perform regression analysis of time series data.

 

LEADERG APP-XGBoost-Regression-Time-Series.png

 

 

 

[Interface functions and descriptions]

 

•    Dataset

 

The drop-down menu shows the datasets that can be analyzed.

 

Selecting dataset.png

 

 

 

•    Open the folder location of the dataset

 

You can quickly edit and add datasets.

 

Open data folder.png

 

 

•    Documentations and instructional videos

 

Open the official website to view the documentations and instructional videos.

 

Docs and Videos.png

 

 

 

•    Program flow

 

Set the parameters of each process and execute in the order of the process.

 

 Program flow.png

 

 

 

[Operation steps and instructions]

 

1.    Select dataset

 

From the drop-down menu, select the dataset you want to analyze.

 

Selecting dataset.png

 

 

 

Introduction to the datasets:

 

•    sales-forecast-airline

 

Airline passenger forecasting.

 

•    stock

 

Stock price prediction, input the opening, closing, high, low, and trading volume, and predict the closing price in five days.

 

 

 

Dataset preparation:

 

•    Training data set

 

File name: train_data.csv

 

File content:

 

The first column is the data index, or the time and date of the time series data. This column will be automatically ignored during analysis.

 

The first N columns are input, and the last column is output (prediction).

 

The following figure is an example of train_input.csv, 1 represents data index, 2 represents input, and 3 represents output.

 

 Data preparation.png

 

 

 

•    Testing dataset

 

File name: inference_data.csv

 

File content:

 

Same as the training dataset.

 

 

 

 

 

2.    In the program flow area 1. Prepare Train Data, set the time sequence parameters and press Run. The input train_data.csv will be performed data augmentation according to the set time sequence parameters and output train_data_time_series.csv.

 

Parameters setting:

 

•    Time Sequence

 

The length of the time series data.

 

 

 

Results:

 

•    Display the size of the training data (train_data.csv) after data augmentation in the console  

 

Data augmentation of training data.png

 

 

 

3.    In the program flow 2. Train, edit the training parameters and press Run to execute the training.

 

Parameters setting:

 

•    Estimator

 

The number of gradient boosted trees (default is 1000).

 

Results:

 

•    Display the Root Mean Squared Error and R-squared of the trained model against the training dataset (train_data_time_series.csv) in the console

 

 Root Mean Squared Error and R-squared_training.png

 

 

 

•    The comparison graph of the predicted value and the true value of the training dataset (train_data_time_series.csv).

 

 Predicted value and true value of the training data.png

 

 

 

•    The scatter plot of the predicted value and the true value of the training dataset (train_data_time_series.csv).

 

 Predicted value and true value scatter plot of the training data.png

 

 

 

•    Output predicted value (train_data_time_series_prediction.csv)

 

After opening the train_data_time_series_prediction.csv file, the last column is the predicted value of the training dataset (train_data_time_series.csv)

 

 Predected values on training data.png

 

 

 

4.    In the program flow 3. Prepare Inference Data, set the time sequence parameters and press Run. The input inference_data.csv will be performed data augmentation according to the set time sequence parameters and output inference_data_time_series.csv.

 

Parameters setting:

 

•    Time Sequence

 

The length of the time series data (This parameter must be the same as the length of the time series during training).

 

 

 

Results:

 

•    Display the size of the testing data (inference_data.csv) after data augmentation in the console

 

 Data augmentation of testing data.png

 

 

 

5.    In the program flow 4. Inference, press Run to execute the inference

 

Parameters setting:

 

•    Estimator

 

The number of gradient boosted trees (default is 1000).

 

 

 

Results:

 

•    Display the Root Mean Squared Error and R-squared of the trained model against the testing dataset (inference_data_time_series.csv) in the console

 

 Root Mean Squared Error and R-squared_testing.png

 

 

 

•    Comparison of the predicted and true values of the testing dataset (inference_data_time_series.csv)

 

 Predicted value and true value of the testing data.png

 

 

 

•    The scatter plot of the predicted and true values of the testing dataset (inference_data_time_series.csv)

 

 Predicted value and true value scatter plot of the testing data.png

 

 

 

•    Output predicted value (inference_data_time_series_prediction.csv)

 

After opening the inference_data_time_series_prediction.csv file, the last column is the predicted value of the testing dataset (inference_data_time_series.csv).

 

 Predected values on testing data.png

 

 

 

 


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