Skip to main content

Time Series & Deep Learning (Part 2 of N): Data Preparation for Training and Evaluation of a LSTM

In the second post of this series we are going to learn how to prepare data for training and evaluation of a LSTM neural network for time series forecasting. Same as for any other post of this series I am referring to Python 3. The data set used is the same as for part 1.
LSTM (Long-Short Term Memory) neural networks are a specialization of RNNs (Recurrent Neural Networks) introduced by Sepp Hochreiter and Jurgen Schmiduber in 1997 to solve the problem of the Vanishing Gradient affecting RNNs. LSTMs are used in real-world applications of language translation, text generation, image captioning, music generation and time series forecasting. You can find more info about LSTMs in my book or wait for one of my next posts of this series. This post focuses mostly on one of the best practices for data preparation before using a data set for training and evaluation of a LSTM in a time series forecasting problem with the Keras library.
Let's load the data set first:

from pandas import read_csv
from pandas import datetime

def parser(x):
    return datetime.strptime(x, '%Y-%m-%d')
features = ['date', 'value']
series = read_csv('./advance-retail-sales-building-materials-garden-equipment-and-supplies-dealers.csv', usecols=features, header=0, parse_dates=[1], index_col=1, squeeze=True, date_parser=parser)


The first action to do is to transform the time series in a way that the forecasting can be threat as a supervised learning problem. In supervised learning typically a data set is divided into input (containing the independent variables) and output (containing the target variable). We are going to use the observation from the previous time step (identified as t-1) as input and the observation at the current time step (identified as t) as output. No need to implement this transformation logic from scratch, as we can use the shift function available for pandas DataFrames. The input variables can be built by shifting of one place down all the values of the original time series. The output is the original time series. Finally we concatenate both series in a DataFrame. Because we need to apply this process to the values of the original data set, it would be good practice to implement a function for it:

from pandas import DataFrame
from pandas import concat

def tsToSupervised(series, lag=1):
    seriesDf = DataFrame(series)
    columns = [seriesDf.shift(idx) for idx in range(1, lag+1)]
    columns.append(seriesDf)
    seriesDf = concat(columns, axis=1)
    seriesDf.fillna(0, inplace=True)
    return seriesDf

supervisedDf = tsToSupervised(series, 1)


Here is a sample of how the supervised DataFrame looks like:

supervisedDf.head()












Is the data set now ready to be used to train and validate the network? Not yet. Other transformations need to be done. But this would be the topic of the next post.
The complete example would be released as a Jupyter notebook at the end of the first part of this series.

Comments

Popular posts from this blog

Streamsets Data Collector log shipping and analysis using ElasticSearch, Kibana and... the Streamsets Data Collector

One common use case scenario for the Streamsets Data Collector (SDC) is the log shipping to some system, like ElasticSearch, for real-time analysis. To build a pipeline for this particular purpose in SDC is really simple and fast and doesn't require coding at all. For this quick tutorial I will use the SDC logs as example. The log data will be shipped to Elasticsearch and then visualized through a Kibana dashboard. Basic knowledge of SDC, Elasticsearch and Kibana is required for a better understanding of this post. These are the releases I am referring to for each system involved in this tutorial: JDK 8 Streamsets Data Collector 1.4.0 ElasticSearch 2.3.3 Kibana 4.5.1 Elasticsearch and Kibana installation You should have your Elasticsearch cluster installed and configured and a Kibana instance pointing to that cluster in order to go on with this tutorial. Please refer to the official documentation for these two products in order to complete their installation (if you do

Exporting InfluxDB data to a CVS file

Sometimes you would need to export a sample of the data from an InfluxDB table to a CSV file (for example to allow a data scientist to do some offline analysis using a tool like Jupyter, Zeppelin or Spark Notebook). It is possible to perform this operation through the influx command line client. This is the general syntax: sudo /usr/bin/influx -database '<database_name>' -host '<hostname>' -username '<username>'  -password '<password>' -execute 'select_statement' -format '<format>' > <file_path>/<file_name>.csv where the format could be csv , json or column . Example: sudo /usr/bin/influx -database 'telegraf' -host 'localhost' -username 'admin'  -password '123456789' -execute 'select * from mem' -format 'csv' > /home/googlielmo/influxdb-export/mem-export.csv

Using Rapids cuDF in a Colab notebook

During last Spark+AI Summit Europe 2019 I had a chance to attend a talk from Miguel Martinez  who was presenting Rapids , the new Open Source framework from NVIDIA for GPU accelerated end-to-end Data Science and Analytics. Fig. 1 - Overview of the Rapids eco-system Rapids is a suite of Open Source libraries: cuDF cuML cuGraph cuXFilter I enjoied the presentation and liked the idea of this initiative, so I wanted to start playing with the Rapids libraries in Python on Colab , starting from cuDF, but the first attempt came with an issue that I eventually solved. So in this post I am going to share how I fixed it, with the hope it would be useful to someone else running into the same blocker. I am assuming here you are already familiar with Google Colab. I am using Python 3.x as Python 2 isn't supported by Rapids. Once you have created a new notebook in Colab, you need to check if the runtime for it is set to use Python 3 and uses a GPU as hardware accelerator. You