Why LSTM for Time Series Forecasting?

Long Short-Term Memory (LSTM), a type of Recurrent neural network (RNN) architecture, was specifically designed to address the vanishing gradient problem that can plague traditional RNN training. Traditional RNNs struggle to learn and remember information over extended periods due to their recurrent connections. This can lead to exploding or vanishing gradients during backpropagation, hindering the learning process.

LSTMs tackle this issue by introducing a memory cell with self-connections, known as the “cell state.” This cell state effectively captures long-term relationships within the data by enabling the network to retain information across lengthy sequences.

Time series forecasting frequently uses LSTM because of its capacity to identify long-term patterns and dependencies in sequential data. The following justifies the suitability of LSTM for time series forecasting:

• Long-term Dependencies: Because LSTMs can retain information over extended periods of time, they are excellent at identifying intricate patterns and dependencies in time series data.
• Non-linear Relationships: LSTMs may learn non-linear relationships and patterns from time series data, which are frequently seen in these types of data.
• Variable-Length Inputs: LSTMs are capable of modelling time series data with varying lengths since they can handle variable-length sequences.
• Feature Learning: By using the input data, LSTMs may automatically extract pertinent features, eliminating the need for human feature engineering.

TensorFlow emerges as a powerful tool for data scientists performing time series analysis through its ability to leverage deep learning techniques. By incorporating deep learning into time series analysis, we can achieve significant advancements in both the depth and accuracy of our forecasts. TensorFlow sits at the forefront of this transformative landscape, offering a robust and versatile platform to construct, train, and deploy these deep neural networks. In this tutorial, we will see how we can leverage LSTM for time series analysis and forecasting.