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Community Blog Time Series Forecasting using variants of Long Short Term Memory (LSTM) Recurrent Neural Networks

Time Series Forecasting using variants of Long Short Term Memory (LSTM) Recurrent Neural Networks

In this article we will walkthrough the implementation of couple of LSTM variants and compare their performances visually against the actual data.

Long Short Term Memory (LSTM) is a recurrent Neural Network which is used for time series forecasting. Time series forecast is used in many applications including stock market prediction, Covid - 19 pandemic new cases prediction, PM 2.5 prediction etc. This comes under the regression category of machine learning problems. The efficiency of the LSTM algorithms in regression problems cannot be limited to the metrics in classification reports. Various researchers use different metrics to analyze the closeness than the exactness of the predicted data against the actual data.

In this article we will walkthrough the implementation of couple of LSTM variants and compare their performances visually against the actual data. DSW Notebook in Machine Learning Platform for AI in Alibaba Cloud is used.

The github link for the source code used for this tutorial is listed here. The most vital libraries used in this code are Tensorflow, Keras and Matplotlib.
Libraries Used
For this tutorial we use a random array with one row of data available.
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The raw_seq array is used to train the LSTM model. The data is set to a chosen number of steps as 10. The number of features n_features is set as 1.
With the training data and n_steps, the data is subjected to the split_sequence function. This is used to split the training data into the number of steps specified.
Splitting Sequence
Now the Stacked LSTM with two layers of LSTM is constructed.
Stack LSTM
The architecture along with its layerwise parameters is given below.
Layers of Stacked LSTM
The variable fut is set as testing data and is longer than the training data. The Stacked LSTM model is run for 200 epochs of training and the testing data is done using mode.predict function.
Execution of Stack LSTM model
The predicted values of Stacked LSTM are stored in buckets1 array. Following this implementation, we create a model for vanilla LSTM which has only one LSTM layer.
Vanilla LSTM creation
The architecture representation of vanilla LSTM is given below.
Vanilla LSTM architecture
This model of Vanilla LSTM is run for 200 epochs on the training data and predicted the testing data as same as the Stacked LSTM and stored in an array variable buckets.
The actual data is plotted against the predicted data of Stacked LSTM and Vanilla LSTM using the matplotlib library.
Code for visualization
The output of prediction looks like the line plot given below.
Line plot of visualized data

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