Skip to content

Latest commit

 

History

History
202 lines (148 loc) · 10.9 KB

README.md

File metadata and controls

202 lines (148 loc) · 10.9 KB
Raw

Forecasting with time series imaging

Xixi Li 2020-05-11

This page introduces how to use our code for image based time series forecasting. The code is divided 2 parts:

  • feature extraction with sift or pretrained CNN
  • model combination based on extracted feature

We completed feature extraction with python because python supports image processing well, while for model combination, we choosed R because R supports statistical forecasting methods very well.

Authorship

  • Xixi Li
  • Yanfei Kang
  • Feng Li

Dependency packages

This code depends some package with specific versions. Before using it, install these packages first.

We use python3.6 for this project. For feature extraction, please install

  • pip install tensorflow==1.2.1
  • pip install scipy==1.2.1
  • pip install opencv-contrib-python==3.4.2.17
  • pip install opencv-contrib-python==3.4.2.17

For model combination, please install xgboost first as follows

  • devtools::install_github("pmontman/customxgboost")

The structure of the project

Usage

1.feature extraction

1.1 feature extraction with sift

In the folder feature_extraction/feature_extraction_with_sift, the function image_based_batch_ts_feature_extraction_with_sift() in the file compute_features_sift.py is used to compute features of time series image.

Parameters explaination for this function image_based_batch_ts_feature_extraction_with_sift(file_path_of_ts,file_path_of_feature,num_cores):

  • file_path_of_ts: the file path of your time series data and it is saved in the format of csv. You can dowoload M4 dataset from here. Then, zip it and put it in feature_extraction/ts-data/M4/MM4DataSet/.
  • file_path_of_feature: the file path where you want to save the extracted feature. In this project, we save the extracted features in feature_extraction/feature_extraction_with_sift/sift-features.
  • num_cores : number of cores that you want to use when parallel computing.

We show an exmple of extracting sift features of M4 mmonthly dataset:

```python
file_path_of_ts='../ts-data/M4/M4DataSet/Monthly-train.csv'
file_path_of_feature='sift-features/M4/M4-monthly-feature-sift.csv'
num_cores=4
image_based_batch_ts_feature_extraction_with_sift(file_path_of_ts,file_path_of_feature,num_cores)
```
The ouput is features of time series images and its shape is n*4200, where n is the number of time series.

1.2 feature extraction with pretrained-CNN

In the folder feature_extraction/feature_extraction_with_pretrained_CNN, the function image_based_batch_ts_feature_extraction_with_sift() in the file compute_features_cnn.py is used to compute features of time series image.

Parameters explaination for function image_based_batch_ts_feature_extraction_with_cnn(file_path_of_ts,file_path_of_feature,cnn_model_name,file_path_of_pretrained_model):

  • file_path_of_ts: the file path of your time series data and it is saved in the format of csv. You can dowoload M4 dataset from here. Then, zip it and put it in feature_extraction/ts-data/M4/MM4DataSet/.
  • file_path_of_feature: the file path where you want to save the extracted feature. In this project, we save the extracted features in feature_extraction/feature_extraction_with_pretrained_CNN/cnn-features.
  • cnn_model_name: which pretrained CNN models you want to use, options include inception_v1 ,resnet_v1 _101,resnet_v1_50 and vgg_19.
  • file_path_of_pretrained_model: file path of pretrained models. You can download all the pretrained models in the following content.

We show an exmple of extracting inception-v1 features of M4 monthly dataset:

```python
file_path_of_ts='../ts-data/M4/M4DataSet/Monthly-train.csv'
file_path_of_feature='cnn-features/M4/M4-monthly-feature-inceptionV1.csv'
cnn_model_name='inception_v1'
file_path_of_pretrained_model='pretrained-models/inception_v1.ckpt'
image_based_batch_ts_feature_extraction_with_cnn(file_path_of_ts,file_path_of_feature,cnn_model_name,file_path_of_pretrained_model)

The ouput is features of time series images and its shape is n*m, where n is the number of time series and m is the dimension of the features.

  • Dimension of the output of the pretrained Inception-v1 model: 1024.
  • Dimension of the output of the pretrained ResNet-v1-101 model: 2048.
  • Dimension of the output of the pretrained ResNet-v1-50 model: 2048.
  • Dimension of the output of the pretrained VGG model: 1000.

pretrained-CNN models

You can download all models here. Otherwise links for individual models can be found below.

These CNNs have been trained on the ILSVRC-2012-CLS image classification dataset.

In the table below, we list each model, the corresponding TensorFlow model file, the link to the model checkpoint。

Model TF-Slim File Checkpoint
Inception V1 Code inception_v1_2016_08_28.tar.gz
ResNet V1 50 Code resnet_v1_50_2016_08_28.tar.gz
ResNet V1 101 Code resnet_v1_101_2016_08_28.tar.gz
VGG 19 Code vgg_19_2016_08_28.tar.gz

2.model combination

Based on the extracted features above, we can do model combination.

In the folder model_combination/R, the main function image.based.model.combination() in the file model.comb.main.R is used to do model combination.

Here, I will brifely introduce the function of other .R files.

  • forec_methods_list.R: it is used to compute the forecasts of 9 candidate methods.
  • process_dataset.R: it is used to compute the accuracy of forecasting methods.
  • combination_ensemble.R: it is used to define the custom loss function of xgboost.
  • ensemble_classifier.R: it is used to train and test the xgboost model.
  • hyperparam.R: hyperparameters optimization for xgboost.
  • hyperparam.search.main.R: an example show how to get opyimal hyperparameters for xgboost.
  • tourism.benchmarks.R: it is used to compute forecasts of the top methods in toursim competition.

Parameters explaination for this function

  image.based.model.combination(
                                #1.which data you'd like to use for training the model, 
                                #you can select 'yearly','quarterly','monthly','weekly','daily' and so on
                                data.type.of.training.data, 
                                
                                #2.which data you'd like to use for testing the model, 
                                #you can select 'yearly','quarterly','monthly','weekly','daily' and so on
                                data.type.of.testing.data,
                                
                                #3.you can choose M4 or tourism
                                training.dataset,
                                
                                #4.you can choose M4 or tourism
                                testing.dataset,
                                
                                #feature type includes sift, inception_v1, resnet101, resnet50 and vgg19
                                feature.type,
                                
                                #5.file path of your training data features, in 'csv' format, shape:n*f, 
                                #where n is the number of time series and f is the dimension of features
                                file.path.of.training.data.features
                                
                                #6.file path of your testing data features, in 'csv' format, shape:n*f,
                                #where n is the number of time series and f is the dimension of features 
                                file.path.of.testing.data.features,
                                
                                #7.file path of the forecasts of 9 methods of training data, in 'rda' 
                                #format, we provide an example in the project
                                file.path.of.training.data.prediction.value,
                                
                                #8.file path of the forecasts of 9 methods of testing data, in 'rda' format,
                                we provide an example in the project
                                file.path.of.testing.data.prediction.value,
                                #9.params for xgboost
                                params)

We show an example of getting forecasts of Tourism monthly data with M4 monthly data as training data:

```r
data.type.of.training.data='Monthly'
data.type.of.testing.data='MONTHLY'
training.dataset=M4
testing.dataset=tourism
feature.type='resnet50'
params=c(14,1,1,0.7052)
file.path.of.training.data.features='C:/xixi/feature_extraction/cnn/cnn-features/M4/Monthly-train-feature-resnet_v1_50.csv'
file.path.of.testing.data.features='C:/xixi/feature_extraction/cnn/cnn-features/Tourism/tourism-monthly-train-feature-resnet_v1_50.csv'
file.path.of.training.data.prediction.value='./forecasts/M4/Monthly_ff.rda'
file.path.of.testing.data.prediction.value='./forecasts/Tourism/MONTHLY_ff.rda'
image.based.model.combination(data.type.of.training.data,
                          data.type.of.testing.data,
                          training.dataset,
                          testing.dataset,
                          feature.type,
                          file.path.of.training.data.features,
                          file.path.of.testing.data.features,
                          file.path.of.training.data.prediction.value,
                          file.path.of.testing.data.prediction.value,
                          params)
```

Then, we can get the forecasting accuracy of the combination method on the targeted dataset.

References