RNN and its Variants in Handling Sequence Data & Time Series Data

Many intriguing patterns and behaviours are underpinned by Within Time. The majority of activities, including human speech, human movement, weather monitoring, forecasting, measuring yearly population, agriculture, and medical data, stock price projections, sales growth, and many more, display a range of serially organised activity sequences. There are algorithms in the field of artificial intelligence that are capable of "seeing things" in advance, such as forecasting future occurrences based on the past. Because of their accuracy and speed, neural networks are regarded as one of the most important machine learning algorithms. It has enabled several developments in artificial intelligence, including robots, speech recognition, picture identification, and many more. These developments were all inspired by how the human brain works. The Artificial Neural Network (ANN) can learn from data and reply with results in the form of recognition, classification, or predictions, much as we are capable of doing so from experience and prior knowledge. Multiple linked neurons, often referred to as processing units, make up a neural network's structure and work together to solve problems. They are effective and adaptable machine learning algorithms because they can learn from examples.

There are different types of Artificial Neural Networks, some of them are:

• Convolutional Neural Network – applied in Image Classification and Object Detection
• Feed-Forward Neural Network – applied in general classification or Regression problems
• Recurrent Neural Network – applied in time series prediction, natural language processing, voice recognition, speech recognition

Information in a feed-forward neural network moves from the input layer via the hidden layers and then to the output layer. These neural network designs are effective for classification, recognition, and other issues where the data is not sequential. As the name implies, this network is simple. Nodes in a feed-forward neural network are not looped with input or held in memory. Only taking into account the most recent information, it is unable to anticipate what will come next or recall anything other than its training.

Figure 1 Feed-Forward and Recurrent Neural Network structure (Source: RNN & LSTM, builtin.com)

In a Recurrent Neural Network (RNN), the information flows in a loop. The network considers the current input and also learns from past inputs. The figure illustrates the flow of information in both networks. The RNN variants hold a memory while the feed-forward doesn’t. The issue with feed-forward can be explained with an example: consider the word “Mangoes” as an input to the feed-forward neural network. As it processes the input by each character and proceeds to reach the last character “s” it has already forgotten, m a n g o e, as a result, feed-forward algorithms don’t work best when it comes to predicting what comes next nor remembering what was previous.

A unique form of neural network is used to make this kind of prediction. The Recurrent Neural Network, or RNN, is thought to be efficient in time-series forecasting and sequential data processing because it has an extra dimension that opens up the potential of temporal dependency. Time-series and sequence data, as opposed to classification or regression issues, increase the complexity of the order as well as the temporal correlation between observations. This necessitates specialised data processing and model evaluation. When it comes to data sequences like time series, text, or speech translation, ANN doesn't do well. The ANN network cannot store any prior knowledge about sequential data in its memory, which prevents it from capturing sequential information. Sequence data is a type of ordered data in which objects that are connected appear one after the other. For instance, the Google autocomplete tool predicts the following word or character as the user types text, which is a form of word prediction. The network receives an image as input and outputs the estimated classification in a straightforward image classification task. You may think of the input and output as one vector. RNN, on the other hand, has the ability to accept a series of inputs and produce a series of vectors as an output. They are adept at seeing trends or impending events. For instance, by feeding RNN the evolution of stock prices over a period of years, it may forecast the prices for the upcoming years. The model can forecast when the market will crash or soar by seeing patterns in the input sequence.RNN was then introduced as an extension to feedforward networks to allow the processing of variable-length sequences. It is unique because of its internal memory to remember key details about the input received. The present and the recent past. This helps it to precisely predict what’s coming next. While feedforward neural networks map one input to one output, RNN is capable of mapping input to output nodes that are one to many (for image captioning), many to many (for machine translation e.g., English to French), or many to one (for Sentiment Classification).

Figure 2 Types of RNN (Source: RNN & LSTM, builtin.com)

The following visualisation demonstrates the RNN process: For ease of comprehension, the neural network is condensed into a single layer of RNN with the following components: X = input nodes, h = hidden layers, y = the output nodes, and t = at a certain time. A loop is used to convey the data from one phase to the next. In order to increase production, the result is then sent back into the network. Thus, as an illustration, "Sour Mangoes are the Best" The term "Sour" is fed into the RNN network at time (t) as the first step. We supply the word "Mangoes" at the time (t1) the next step. Once every word in the phrase has been sent into the network, the procedure is repeated. RNN has knowledge of all the words that came before it in the final stage.

Figure 3 Simple Recurrent Neural Network (Source: RNN tutorial, simplilearn.com)

When applying this architecture in language translation, a typical sentence may be longer than 5 words, resulting in a long temporal gap from the first input word till it reaches a step where it uses that to make a prediction. With increasing number of steps, there is a challenge in retaining information from the previous steps. The model takes too long to learn, this results in RNN’s short-term memory issue. It also raises an issue known as Vanishing Gradients. This happens when the gradient values are ‘vanishing’ or too small to effectively train the model. RNN is trained using Backpropagation through time allowing the network to adjust weights, decrease errors and improve the learning during the training process. To adjust input- to-hidden weights based on the first input, the signal needs to travel backward through its pathway in the network. During each step of backpropagation, a gradient value is calculated to compare the outcome to our desired output. During the Backpropagation, if the gradient value is too low reaching zero, the harder it is for the network to update its weights and reach an outcome. Similarly, if the gradient values are too high or go to infinity, it makes the process unstable and raises an exploding gradient problem. To resolve such issues, many variants of RNN are introduced. Click here to learn Data Analytics in Hyderabad

Figure 4 RNN short-term Memory issue (Source: LSTM from zero to hero, floyhub.com)

RNN Variants

• Long short-term memory (LSTM)

  The Recurrent Neural Network is changed in the Long Short-Term Memory, or LSTM. Memory blocks that can read, write, and erase data are the foundation of its architecture. To regulate the learning process, the LSTM employs a system of "gates." The input, output, and forget gates are shown as such in figure 6 below. For long-term access and storage of information, these gates serve as memory cells. Based on the weights it learns, the gates determine whether information should be stored or removed. The input gate will receive both fresh information (X(t)) and information from a prior concealed state (X(t-1)). The short-term memory and input are sent via a "Sigmoid Function" to determine which information can get through and what information can be eliminated.

  

  Figure 5 LSTM storing features for long-term (Source: LSTM from hero to zero, floyhub.com)

  

  Figure 6 LSTM and its three gates (Source: RNN & LSTM, builtin.com)

• GRU

  Another variant of RNN known as Gated Feedback Recurrent Neural Networks or GRU works very similar to LSTM. Just like LSTM, it is capable of effectively retaining long-term dependencies in sequential data. It is considered faster with effectiveness and accuracy. It uses gate mechanisms to regulate the flow of information between ‘cells’ in the neural network. There is a disparity in the structure of the two variants. GRU structure consists of two gates, the Reset Gate and the Update Gate. It merges the Memory and output state. The reset gate (r) controls the information that flows out or is discarded. The forget gate and input gate are combined to form an update gate (z) for regulating the information to be retained from previous memory and also the new memory to be added. By reducing the parameters GRU computation is a little more efficient, simpler, and faster. Click here to learn Data Science in Bangalore

  

  Figure 7 GRU Structure (Source: RNN, Tingwu Wang)

  Consider the following example to better understand it: A trainee is evaluating a training programme. I enrolled in this training course last summer. "I really enjoyed the programme," the review concludes after a few more lines and words. The programme was well-structured to cover all important parts of the subject, and the instructors are personable. The last two lines of the review are all that the model requires to calculate the trainee's degree of satisfaction. This text is read and calculated when it is sent to the model as input to decide which data to gather from the contents of the current timestep's memory and prior timesteps. The model may choose which information to keep and which information to discard by setting a reset gate vector value close to zero. As a result, the review's opening paragraphs can be disregarded while the review's crucial last two phrases are respected.

  In tackling complicated time series and sequential data processing scenarios, this article briefly presented RNN and some of its variations. State-of-the-art models used in neural language processing, voice recognition, and other types of deep learning include LSTM and GRU. They are very good at processing sequence and time-series data because they can control the flow of information in a chain of sequence using the Gates mechanism.

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