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An implementation of the DeepRenewal Processes in GluonTS

Project description

GluonTS Implementation of Deep Renewal Processes

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Intermittent Demand Forecasting with Deep Renewal Processes Ali Caner Turkmen, Yuyang Wang, Tim Januschowski

Table of Contents

Installation

Recommended Python Version: 3.6

    pip install deeprenewal

If you are working Windows and need to use your GPU(which I recommend), you need to first install MXNet==1.6.0 version which supports GPU MXNet Official Installation Page

And if you are facing difficulties installing the GPU version, you can try(depending on the CUDA version you have)

pip install mxnet-cu101==1.6.0 -f https://dist.mxnet.io/python/all

Relevant Github Issue

The sources for DeepRenewal can be downloaded from the Github repo_.

You can either clone the public repository:

git clone git://github.com/manujosephv/deeprenewal

Once you have a copy of the source, you can install it with:

python setup.py install

Dataset

Download

Retail Dataset

Description

It is a transactional data set which contains all the transactions occurring between 01/12/2010 and 09/12/2011 for a UK-based and registered non-store online retail. The company mainly sells unique all-occasion gifts. Many customers of the company are wholesalers.

Columns:

  • InvoiceNo: Invoice number. Nominal, a 6-digit integral number uniquely assigned to each transaction. If this code starts with letter ‘c’, it indicates a cancellation.
  • StockCode: Product (item) code. Nominal, a 5-digit integral number uniquely assigned to each distinct product.
  • Description: Product (item) name. Nominal.
  • Quantity: The quantities of each product (item) per transaction. Numeric.
  • InvoiceDate: Invice Date and time. Numeric, the day and time when each transaction was generated.
  • UnitPrice: Unit price. Numeric, Product price per unit in sterling.
  • CustomerID: Customer number. Nominal, a 5-digit integral number uniquely assigned to each customer.
  • Country: Country name. Nominal, the name of the country where each customer resides.

Preprocessing:

  • Group by at StockCode, Country, InvoiceDate –> Sum of Quantity, and Mean of UnitPrice
  • Filled in zeros to make timeseries continuous
  • Clip lower value of Quantity to 0(removing negatives)
  • Took only Time series which had length greater than 52 days.
  • Train Test Split Date: 2011-11-01

Stats:

  • No. of Timeseries: 3828. After filtering: 3671
  • Quantity: Mean = 3.76, Max = 12540, Min = 0, Median = 0
  • Heavily Skewed towards zero

Time Series Segmentation

Segmentation

We can see that almost 98% of the timeseries in the dataset are either Intermittent or Lumpy, which is perfect for our use case.

Model

Architecture

Usage

Train with CLI

usage: deeprenewal [-h] [--use-cuda USE_CUDA] 
                   [--datasource {retail_dataset}]
                   [--regenerate-datasource REGENERATE_DATASOURCE]
                   [--model-save-dir MODEL_SAVE_DIR]
                   [--point-forecast {median,mean}]
                   [--calculate-spec CALCULATE_SPEC] 
                   [--batch_size BATCH_SIZE]
                   [--learning-rate LEARNING_RATE] 
                   [--max-epochs MAX_EPOCHS]
                   [--number-of-batches-per-epoch NUMBER_OF_BATCHES_PER_EPOCH]
                   [--clip-gradient CLIP_GRADIENT]
                   [--weight-decay WEIGHT_DECAY]
                   [--context-length-multiplier CONTEXT_LENGTH_MULTIPLIER]
                   [--num-layers NUM_LAYERS] 
                   [--num-cells NUM_CELLS]
                   [--cell-type CELL_TYPE] 
                   [--dropout-rate DROPOUT_RATE]
                   [--use-feat-dynamic-real USE_FEAT_DYNAMIC_REAL]
                   [--use-feat-static-cat USE_FEAT_STATIC_CAT]
                   [--use-feat-static-real USE_FEAT_STATIC_REAL]
                   [--scaling SCALING]
                   [--num-parallel-samples NUM_PARALLEL_SAMPLES]
                   [--num-lags NUM_LAGS] 
                   [--forecast-type FORECAST_TYPE]

GluonTS implementation of paper 'Intermittent Demand Forecasting with Deep
Renewal Processes'

optional arguments:
  -h, --help            show this help message and exit
  --use-cuda USE_CUDA
  --datasource {retail_dataset}
  --regenerate-datasource REGENERATE_DATASOURCE
                        Whether to discard locally saved dataset and
                        regenerate from source
  --model-save-dir MODEL_SAVE_DIR
                        Folder to save models
  --point-forecast {median,mean}
                        How to estimate point forecast? Mean or Median
  --calculate-spec CALCULATE_SPEC
                        Whether to calculate SPEC. It is computationally
                        expensive and therefore False by default
  --batch_size BATCH_SIZE
  --learning-rate LEARNING_RATE
  --max-epochs MAX_EPOCHS
  --number-of-batches-per-epoch NUMBER_OF_BATCHES_PER_EPOCH
  --clip-gradient CLIP_GRADIENT
  --weight-decay WEIGHT_DECAY
  --context-length-multiplier CONTEXT_LENGTH_MULTIPLIER
                        If context multipler is 2, context available to hte
                        RNN is 2*prediction length
  --num-layers NUM_LAYERS
  --num-cells NUM_CELLS
  --cell-type CELL_TYPE
  --dropout-rate DROPOUT_RATE
  --use-feat-dynamic-real USE_FEAT_DYNAMIC_REAL
  --use-feat-static-cat USE_FEAT_STATIC_CAT
  --use-feat-static-real USE_FEAT_STATIC_REAL
  --scaling SCALING     Whether to scale targets or not
  --num-parallel-samples NUM_PARALLEL_SAMPLES
  --num-lags NUM_LAGS   Number of lags to be included as feature
  --forecast-type FORECAST_TYPE
                        Defines how the forecast is decoded. For details look
                        at the documentation

An example of training process is as follows:

python3 deeprenewal --datasource retail_dataset --lr 0.001 --epochs 50

Train with Jupyter Notebook

Check out the examples folder for notebooks

Result

Method QuantileLoss[0.25] QuantileLoss[0.5] QuantileLoss[0.75] mean_wQuantileLoss
Croston 664896.9323 791880.3858 918863.8392 1.034257626
SBA 623338.1011 776084.5519 928831.0028 1.013627034
SBJ 627880.7754 779758.6188 931636.4622 1.018425652
ARIMA 598779.2977 784662.7412 957980.814 1.019360367
ETS 622502.7789 796128.4 957808.4087 1.03460523
DeepAR 378217.1822 679862.7643 808336.3482 0.812561813
NPTS 380956 725255 935102.5 0.88870495
DeepRenewal Flat 383524.4007 764167.8638 1047169.894 0.955553796
DeepRenewal Exact 382825.5 765640 1141210.5 0.99683189
DeepRenewal Hybrid 389981.2253 761474.4966 1069187.032 0.96677762

Blog

For a more detailed account of the implementation and the experiments please visit the blog:

References

[1] Ali Caner Turkmen, Yuyang Wang, Tim Januschowski. "Intermittent Demand Forecasting with Deep Renewal Processes". arXiv:1911.10416 [cs.LG] (2019) [2] Alexander Alexandrov, Konstantinos Benidis, Michael Bohlke-Schneider, Valentin Flunkert, Jan Gasthaus, Tim Januschowski, Danielle C. Maddix, Syama Rangapuram, David Salinas, Jasper Schulz, Lorenzo Stella, Ali Caner Türkmen, Yuyang Wang;. "GluonTS: Probabilistic and Neural Time Series Modeling in Python". (2020).

History

0.3.1 (2020-10-15)

  • Dependencies were not getting included. Fixed that

0.3.0 (2020-10-15)

  • Fixed a build error

0.2.0 (2020-10-15)

0.1.2 (2020-10-13)

  • Minor changes in documentation.

0.1.0 (2020-10-13)

  • First release on PyPI.

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