[LEADERG APP] LSTM

Language:

繁體中文

 

 

[Introduction]

 

Use LSTM (Long Short-Term Memory) to analyze time series data.

 

 LEADERG APP-LSTM.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_input.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_input.csv

 

File content:

 

Same as the training dataset.

 

 

 

 

 

2.    In the program flow 1. Train, edit the training parameters and press Run to execute the training

 

Parameters setting：

 

•    Time Sequence

 

The length of the time series data (default is 12).

 

•    Learning Rate

 

Learning rate for training (default is 0.01).

 

•    Epochs

 

The number of iterations of training (default is 20000).

 

 

 

Results：

 

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

 

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

 

 

 

•    Display the training loss (Loss) in the console.

 

 Training loss.png

 

 

 

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

 

 Root Mean Squared Error and R-squared_training.png

 

 

 

•    Output predicted value (train_output.csv)

 

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

 

 Predected values on training data.png

 

 

 

3.    In the program flow 2. Inference, press Run to execute the inference

 

Parameters setting：

 

•    Time Sequence

 

The length of the time series data (default is 12).

 

This parameter must be the same as the length of the time series during training.

 

 

 

Results:

 

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

 

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

 

 

 

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

 

 Root Mean Squared Error and R-squared_testing.png

 

 

 

•    Output predicted value (inference_output.csv)

 

After opening the inference _output.csv file, the last column is the predicted value of the testing dataset (inference _input.csv).

 

 Predected values on testing data.png

 

 

 

 


How to Buy

Welcome to contact us for quotation. We will help you buy right products.
Email: leaderg@leaderg.com

Thanks for our customers

Taiwan University, Tsing Hua University, Yang Ming Chiao Tung University, Cheng Kung University, Taipei Medical University, Taipei University of Nursing and Health Sciences, National Chung Hsing University, Chi Nan University, Ilan University, United University, Defence University, Naval Academy, Feng Chia University, Chang Gung University, I-Shou University, Shih Chien University, Taiwan University of Science and Technology, Taichung University of Science and Technology, Yunlin University of Science and Technology, Chin-Yi University of Science, Formosa University, Pintung University of Science and Technology, Kaohsiung University of Science and Technology, Chaoyang University of Technology, Ming Chi University of Technology, Southern Taiwan University of Science and Technology, Taipei Veterans General Hospital, Chang Gung Medical Foundation, Tzu Chi Medical Foundation, E-Da Hospital, Chung-Shan Institute of Science and Technology, Armaments Bureau, Ministry of Justice Investigation Bureau, Institute of Nuclear Energy Research, Endemic Species Research Institute, Institute of Labor, Occupational Safety And Health, Metal Industries Research & Development Centre, Taiwan Instrument Research Institute, Automotive Research & Testing Center, Taiwan Water Corporation, Taiwan Semiconductor Manufacturing Co., Ltd., United Microelectronics Corp., Nanya Technology, Winbond Electronics Corp., Xintec Inc., Arima Lasers Corporation, AU Optronics Corporation, Innolux Corporation, HannStar Display Corporation, Formosa Plastics Group., Formosa Technologies Corporation, Nan Ya Plastics Corp., Formosa Chemicals & Fibre Corporation, Chinese Petroleum Corporation, Logitech, ELAN Microelectronics Corp., Lextar Electronics Corporation, Darfon Electronics Corp., WPG Holdings, Mirle Automation Corporation, Symtek Automation Asia Co., Ltd, ChipMOS Technologies Inc., Dynapack International Technology Corporation, Primax Electronics Ltd., Grade Upon Technology Corp., AAEON Technology Inc., Stark Technology, Inc., Horng Terng Automation Co., Ltd., Zhen Ding Technology Holding Ltd, Boardtek Electronics Corporation, MiTAC International Corporation, Allion Labs, Inc., Sound Land Corp., Hong Hu Tech, etc.