AI News, A Guide For Time Series Prediction Using Recurrent Neural Networks (LSTMs)

A Guide For Time Series Prediction Using Recurrent Neural Networks (LSTMs)

As an Indian guy living in the US, I have a constant flow of money from home to me and vice versa.

If one can predict how much a dollar will cost tomorrow, then this can guide one’s decision making and can be very important in minimizing risks and maximizing returns.

Looking at the strengths of a neural network, especially a recurrent neural network, I came up with the idea of predicting the exchange rate between the USD and the INR.

There are a lot of methods of forecasting exchange rates such as: In this article, we’ll tell you how to predict the future exchange rate behavior using time series analysis and by making use of machine learning with time series.

The simplest recurrent neural network can be viewed as a fully connected neural network if we unroll the time axes.

This formula is like the exponential weighted moving average (EWMA) by making its pass values of the output with the current values of the input.

As we have talked about, a simple recurrent network suffers from a fundamental problem of not being able to capture long-term dependencies in a sequence.

In late ’90s, LSTM was proposed by Sepp Hochreiter and Jurgen Schmidhuber, which is relatively insensitive to gap length over alternatives RNNs, hidden markov models, and other sequence learning methods in numerous applications.

Forget Gate It is a sigmoid layer that takes the output at t-1 and the current input at time t and concatenates them into a single tensor and applies a linear transformation followed by a sigmoid.

This layer applies a hyperbolic tangent to the mix of input and previous output, returning a candidate vector to be added to the internal state.

The internal state is updated with this rule: .The previous state is multiplied by the forget gate and then added to the fraction of the new candidate allowed by the output gate.

These three gates described above have independent weights and biases, hence the network will learn how much of the past output to keep, how much of the current input to keep, and how much of the internal state to send out to the output.

Over a period of time, a recurrent neural network tries to learn what to keep and how much to keep from the past, and how much information to keep from the present state, which makes it so powerful as compared to a simple feed forward neural network.

Many of the newer developed economies suffered far less impact, particularly China and India, whose economies grew substantially during this period.

fully Connected Model is a simple neural network model which is built as a simple regression model that will take one input and will spit out one output.

As a loss function, we use mean squared error and stochastic gradient descent as an optimizer, which after enough numbers of epochs will try to look for a good local optimum.

After training this model for 200 epochs or early_callbacks (whichever came first), the model tries to learn the pattern and the behavior of the data.

Since we split the data into training and testing sets we can now predict the value of testing data and compare them with the ground truth.

We used 6 LSTM nodes in the layer to which we gave input of shape (1,1), which is one input given to the network with one value.

This model has learned to reproduce the yearly shape of the data and doesn’t have the lag it used to have with a simple feed forward neural network.

Sliding time window methods are very useful in terms of fetching important patterns in the dataset that are highly dependent on the past bulk of observations.

LSTM models are powerful enough to learn the most important past behaviors and understand whether or not those past behaviors are important features in making future predictions.

A Beginner's Guide to Neural Networks and Deep Learning

Contents Neural networks are a set of algorithms, modeled loosely after the human brain, that are designed to recognize patterns.

so you can think of deep neural networks as components of larger machine-learning applications involving algorithms for reinforcement learning, classification and regression.) What kind of problems does deep learning solve, and more importantly, can it solve yours?

It is known as a “universal approximator”, because it can learn to approximate an unknown function f(x) = y between any input x and any output y, assuming they are related at all (by correlation or causation, for example).

In the process of learning, a neural network finds the right f, or the correct manner of transforming x into y, whether that be f(x) = 3x + 12 or f(x) = 9x - 0.1.

A node combines input from the data with a set of coefficients, or weights, that either amplify or dampen that input, thereby assigning significance to inputs for the task the algorithm is trying to learn.

(For example, which input is most helpful is classifying data without error?) These input-weight products are summed and the sum is passed through a node’s so-called activation function, to determine whether and to what extent that signal progresses further through the network to affect the ultimate outcome, say, an act of classification.

Therefore, one of the problems deep learning solves best is in processing and clustering the world’s raw, unlabeled media, discerning similarities and anomalies in data that no human has organized in a relational database or ever put a name to.

For example, deep learning can take a million images, and cluster them according to their similarities: cats in one corner, ice breakers in another, and in a third all the photos of your grandmother.

Given that feature extraction is a task that can take teams of data scientists years to accomplish, deep learning is a way to circumvent the chokepoint of limited experts.

When training on unlabeled data, each node layer in a deep network learns features automatically by repeatedly trying to reconstruct the input from which it draws its samples, attempting to minimize the difference between the network’s guesses and the probability distribution of the input data itself.

In the process, these networks learn to recognize correlations between certain relevant features and optimal results – they draw connections between feature signals and what those features represent, whether it be a full reconstruction, or with labeled data.

(Bad algorithms trained on lots of data can outperform good algorithms trained on very little.) Deep learning’s ability to process and learn from huge quantities of unlabeled data give it a distinct advantage over previous algorithms.

The starting line for the race is the state in which our weights are initialized, and the finish line is the state of those parameters when they are capable of producing accurate classifications and predictions.

collection of weights, whether they are in their start or end state, is also called a model, because it is an attempt to model data’s relationship to ground-truth labels, to grasp the data’s structure.

(You can think of a neural network as a miniature enactment of the scientific method, testing hypotheses and trying again – only it is the scientific method with a blindfold on.) Here is a simple explanation of what happens during learning with a feedforward neural network, the simplest architecture to explain.

The neural then takes its guess and compares it to a ground-truth about the data, effectively asking an expert “Did I get this right?” The difference between the network’s guess and the ground truth is its error.

The three pseudo-mathematical formulas above account for the three key functions of neural networks: scoring input, calculating loss and applying an update to the model – to begin the three-step process over again.

In its simplest form, linear regression is expressed as where Y_hat is the estimated output, X is the input, b is the slope and a is the intercept of a line on the vertical axis of a two-dimensional graph.

It’s typically expressed like this: (To extend the crop example above, you might add the amount of sunlight and rainfall in a growing season to the fertilizer variable, with all three affecting Y_hat.) Now, that form of multiple linear regression is happening at every node of a neural network.

The output of all nodes, each squashed into an s-shaped space between 0 and 1, is then passed as input to the next layer in a feed forward neural network, and so on until the signal reaches the final layer of the net, where decisions are made.

The name for one commonly used optimization function that adjusts weights according to the error they caused is called “gradient descent.” Gradient is another word for slope, and slope, in its typical form on an x-y graph, represents how two variables relate to each other: rise over run, the change in money over the change in time, etc.

the signal of the weight passes through activations and sums over several layers, so we use the chain rule of calculus to march back through the networks activations and outputs and finally arrive at the weight in question, and its relationship to overall error.

That is, given two variables, Error and weight, that are mediated by a third variable, activation, through which the weight is passed, you can calculate how a change in weight affects a change in Error by first calculating how a change in activation affects a change in Error, and how a change in weight affects a change in activation.

(We’re 120% sure of that.) As the input x that triggers a label grows, the expression e to the x shrinks toward zero, leaving us with the fraction 1/1, or 100%, which means we approach (without ever quite reaching) absolute certainty that the label applies.

Input that correlates negatively with your output will have its value flipped by the negative sign on e’s exponent, and as that negative signal grows, the quantity e to the x becomes larger, pushing the entire fraction ever closer to zero.

You can set different thresholds as you prefer – a low threshold will increase the number of false positives, and a higher one will increase the number of false negatives – depending on which side you would like to err.

That said, gradient descent is not recombining every weight with every other to find the best match – its method of pathfinding shrinks the relevant weight space, and therefore the number of updates and required computation, by many orders of magnitude.

A neural network is a powerful computational data model that is able to capture and represent complex input/output relationships.

development of neural network technology stemmed from the desire to develop an artificial system that could perform 'intelligent' tasks similar to those

Neural networks resemble the human brain in the following two ways: The true power and advantage of neural networks lies in their ability to represent both linear and non-linear relationships and in their ability to learn

The goal of this type of network is to create a model that correctly maps the input to the output using historical data so

As the processed data leaves the first hidden layer, again it gets multiplied by interconnection weights, then summed

Finally the data is multiplied by interconnection weights then processed one last time within the output layer

With each presentation the output of the neural network is compared to the desired output and an error is computed.

error is then fed back (backpropagated) to the neural network and used to adjust the weights such that the error decreases with each iteration and the

each presentation, the error between the network output and the desired output is computed and fed back to the neural network.

software must analyze each group of pixels (0's and 1's) that form a letter and produce a value that corresponds to that letter.

data-intensive applications, such as: NeuroDimension has been in the business of bringing neural networks and predictive data analytics to individuals, businesses, and universities from around the

It accesses your data, cleans it, organizes it, manipulates it, and intelligently searches through the most popular neural networks.

It also features next generation distributed and parallel computing using as many computers and processors as you want at discovering relationships.

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An Artificial Neural Network (ANN) is a computational model that is inspired by the way biological neural networks in the human brain process information.

Artificial Neural Networks have generated a lot of excitement in Machine Learning research and industry, thanks to many breakthrough results in speech recognition, computer vision and text processing.

The output Y from the neuron is computed as shown in the Figure 1. The function f is non-linear and is called the Activation Function. The purpose of the activation function is to introduce non-linearity into the output of a neuron.

This is important because most real world data is non linear and we want neurons to learn these non linear representations.

Every activation function (or non-linearity) takes a single number and performs a certain fixed mathematical operation on it [2].

There are several activation functions you may encounter in practice: σ(x) = 1 / (1 + exp(−x)) tanh(x) = 2σ(2x) − 1 f(x) = max(0, x) The below figures [2]  show each of the above activation functions.

Importance of Bias: The main function of Bias is to provide every node with a trainable constant value (in addition to the normal inputs that the node receives).

feedforward neural network can consist of three types of nodes: In a feedforward network, the information moves in only one direction –

There are no cycles or loops in the network [3] (this property of feed forward networks is different from Recurrent Neural Networks in which the connections between the nodes form a cycle).

Hidden Layer: The Hidden layer also has three nodes with the Bias node having an output of 1. The output of the other two nodes in the Hidden layer depends on the outputs from the Input layer (1, X1, X2) as well as the weights associated with the connections (edges).

Given a set of features X = (x1, x2, …) and a target y, a Multi Layer Perceptron can learn the relationship between the features and the target, for either classification or regression.

The Final Result column can have two values 1 or 0 indicating whether the student passed in the final term. For example, we can see that if the student studied 35 hours and had obtained 67 marks in the mid term, he / she ended up passing the final term.

Now, suppose, we want to predict whether a student studying 25 hours and having 70 marks in the mid term will pass the final term.

This is a binary classification problem where a multi layer perceptron can learn from the given examples (training data) and make an informed prediction given a new data point.

Suppose the output probabilities from the two nodes in the output layer are 0.4 and 0.6 respectively (since the weights are randomly assigned, outputs will also be random).

Step 2: Back Propagation and Weight Updation We calculate the total error at the output nodes and propagate these errors back through the network using Backpropagation to calculate the gradients.

If we now want to predict whether a student studying 25 hours and having 70 marks in the mid term will pass the final term, we go through the forward propagation step and find the output probabilities for Pass and Fail.

The network takes 784 numeric pixel values as inputs from a 28 x 28 image of a handwritten digit (it has 784 nodes in the Input Layer corresponding to pixels).

The network has 300 nodes in the first hidden layer, 100 nodes in the second hidden layer, and 10 nodes in the output layer (corresponding to the 10 digits) [15].

Although the network described here is much larger (uses more hidden layers and nodes) compared to the one we discussed in the previous section, all computations in the forward propagation step and backpropagation step are done in the same way (at each node) as discussed before.

Notice how in the output layer, the only bright node corresponds to the digit 5 (it has an output probability of 1, which is higher than the other nine nodes which have an output probability of 0).

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