Developing a predictive model for time series data
RNNs, specifically LSTM models, is often a difficult topic to understand. Time series prediction is a useful application of RNNs because of temporal dependencies in the data. Time series data is abundantly available online. In this section, we will see an example of using an LSTM for handling time series data. Our LSTM network will be able to predict the number of airline passengers in the future.
Description of the dataset
The dataset that I will be using is data about international airline passengers from 1949 to 1960. The dataset can be downloaded from https://datamarket.com/data/set/22u3/international-airlinepassengers- monthly-totals-in#!ds=22u3&display=line. The following screenshot shows the metadata of the international airline passengers:
Figure 14: Metadata of the international airline passengers (source: https://datamarket.com/)
You can download the data by choosing the Export tab and then selecting CSV (,) in the Export group...
