This page contains the explanation of our Long Short-Term Memory Multi-Seasonal Net (LSTM-MSNet) forecasting framework, which can be used to forecast a sets of time series with multiple seasonal patterns.

In the description, we first provide a breif introduction to our methdology, and then explain the steps to be followed to execute our code and use our framework for your research work.

The above figure gives an overview of the proposed LSTM-MSNet training paradigms. In the DS approach, deseasonalised time series are used to train the LSTM-MSNet. Here, a reseasonalisation phase is required as the target MW patches are seasonally adjusted. Whereas in the SE approach, the seasonal values extracted from the deseasonalisation phase are employed as exogenous variables, along with the original time series to train the LSTM-MSNet. Here a reseasonalisation phase is not required as the target MW patches contain the original distribution of the time series. A more detailed explaination of these training paradigms can be found in our manuscript.

We used DS and SE naming conventions in our code repository to distinguish these training paradigms. Please note that this repo contains seperate preprocessing files for each of these training paradigms.

As illustrated in the above figure, the LSTM-MSNet framework consists of three main phases: i) pre-processing phase: using state-of-the-art multi-seasonal decomposition techniques, i.e., MSTL, Prophet, Tbats to extract the seasonal components. Additonally, for the SE approach fourier terms have used to denote the seasonal trajectories (in order to supplement the subsequent LSTM training phase) ii) training phase: LSTM-MSNet framework training and iii) post-processing phase: retransform the forecasts into original scale.

Set the PYTHONPATH env variable of the system. Append absolute paths of both the project root directory and the directory of the external_packages/cocob_optimizer into the PYTHONPATH

Three files need to be created for every model, one per training, validation and testing. For R scripts (under src/LSTM-Preprocessing-Scripts), make sure to set the working directory to the project root folder. As an example, solar_train.txt file is hardcoded in the scripts. The current source code supports for comma seperated data input, however this can be easily adjustable for other delimiters.

We assume solar_train.txt contain hourly energy consumption observations of multiple households. Each time series consists of 2 years of hourly data, and may present three types of seasonalities; daily, weekly, and yearly. As explained earlier, SE and DS folders denote the two different paradigms. Whereas, Baseline folder denotes a varaint that does not use any paradigm when training the LSTM-MSNet.

When training the LSTM-MSNet, we use the tfrecords function provided by the Tensorflow API for a faster execution of our models. The preprocessing scripts used to generate the tfrecords can be found in the src/LSTM-Models/preprocess_scripts directory. The src/LSTM-Models/tfrecords_handler/moving_window module converts the text data into tfrecords format (using energy_tfrecord_DS_writer.py/energy_tfrecord_SE_writer.py) as well as reads in tfrecord data (using energy_tfrecord_DS_reader.py/energy_tfrecord_SE_reader.py) during execution.

Sample Record of validation file in moving window format:

1|i -0.120404761911659 -0.138029849544217 -0.158262315258994 -0.117573911196581 -0.047514354625692 -0.054921000563831 -0.087502195404757 -0.0468948356427585 -0.0265522120279886 -0.0259454546421436 -0.0149743425531481 -0.0750882944138711 0.0300152959591582 0.037022158965339 0.0168685236725015 |o -0.0487566395401897 -0.00313169841363781 -0.0356365611845675 0.11323901494058 0.0498791083802557 -0.0222170965043569 0.0324163879281905 0.0353096916266837 0.085162256512592 0.0644041024983562 0.0970988030382305 0.100330957527596 |# 6.88534358640275 -0.00313977170055892 -0.0044384039516765 0.00223114486678285 0.00574405742601041 0.00832797755707766 0.00264786188838812 0.00821557645548867 0.0196038788714076 -0.0082329067304395 -0.0136679361428553 -0.00526828286265864 -0.0120231978314266

Example bash script:

python ./generic_model_energy_DS_trainer.py --dataset_name energy_ds --contain_zero_values 0 --initial_hyperparameter_values_file configs/initial_hyperparameter_values/energy_hourly --binary_train_file_train_mode datasets/binary_data/energy_ds/moving_window/energy_ds_30i24.tfrecords --binary_valid_file_train_mode datasets/binary_data/energy_ds/moving_window/energy_ds_30i24v.tfrecords --binary_train_file_test_mode datasets/binary_data/energy_ds/moving_window/energy_ds_12i15v.tfrecords --binary_test_file_test_mode datasets/binary_data/CIF_2016/moving_window/energy_dstest.tfrecords --txt_test_file datasets/text_data/CIF_2016/moving_window/energy_test.txt --actual_results_file datasets/text_data/CIF_2016/energy_results.txt --input_size 30 --forecast_horizon 24 --optimizer cocob -- cell_type LSTM --hyperparameter_tuning smac --model_type stacking --input_format moving_window --seasonality_period 8766 --original_data_file datasets/text_data/CIF_2016/energy_train.txt --seed 1234

The model expects a number of arguments.

1. dataset_name - Any unique string for the name of the dataset
2. contain_zero_values - Whether the dataset contains zero values(0/1)
3. initial_hyperparameter_values_file - The file for the initial hyperparameter range configurations
4. binary_train_file_train_mode - The tfrecords file for train dataset in the training mode
5. binary_valid_file_train_mode - The tfrecords file for validation dataset in the training mode
6. binary_train_file_test_mode - The tfrecords file for train dataset in the testing mode
7. binary_test_file_test_mode - The tfrecords file for test dataset in the testing mode
8. txt_test_file - The text file for test dataset
9. actual_results_file - The text file of the actual results
10. original_data_file - The text file of the original dataset with all the given data points
11. cell_type - The cell type of the RNN(LSTM/GRU/RNN). Default is LSTM
12. input_size - The input size of the moving window. Default is 0 in the case of non moving window format
13. seasonality_period - The highest seasonality period of the time series (to calculate MASE)
14. forecast_horizon - The forecast horizon of the dataset
15. optimizer - The type of the optimizer(we only use cocob optimiser)
16. model_type - The type of the model(we only use stacking architecture)
17. input_format - Input format(we only use moving_window format)
18. seed - Integer seed to use as the random seed for hyperparameter tuning

The first point of invoking the models is the generic_model_energy_DS_trainer.py/generic_model_energy_SE_trainer.py (for the simplicity of explaination, we use only the DS training paradigm scenario). The generic_model_energy_DS_trainer.py parses the external arguments and identifies the required type of model, optimizer, cell etc... The actual models are inside the directory src/LSTM-Models/rnn_architectures/stacking_model/. First, the hyperparameter tuning is carried out using the validation errors of the respective model trainer. Example initial hyperparameter ranges can be found inside the directory src/LSTM-Models/configs/initial_hyperparameter_values. The found optimal hyperparameter combination is written to a file in the directory src/LSTM-Models/results/optimized_configurations/. Then the found optimal hyperparameter combination is used on the respective model tester to generate the final forecasts. The final forecasts is written to the src/LSTM-Models/results/rnn_forecasts directory.

The SMAPE and MASE errors are calculated per each series can be calculated using the solar_test.txt, given the RNN forecast generated in the src/LSTM-Models/results/rnn_forecasts repository.

When using this repository, please cite:

@article{Bandara2019-si,
  title={{LSTM-MSNet}: Leveraging Forecasts on Sets of Related Time Series with Multiple Seasonal Patterns},
  author={Bandara, Kasun, Bergmeir, Christoph, and Hewamalage, Hansika},
  journal={arXiv preprint arXiv:1909.04293},
  year={2019}
}