The Statsbot team has already published the article about using time series analysis for anomaly detection. Today, we’d like to discuss time series prediction with a long short-term memory model (LSTMs). We asked a data scientist, Neelabh Pant, to tell you about his experience of forecasting exchange rates using recurrent neural networks.
As an Indian guy living in the US, I have a constant flow of money from home to me and vice versa. If the USD is stronger in the market, then the Indian rupee (INR) goes down, hence, a person from India buys a dollar for more rupees. If the dollar is weaker, you spend less rupees to buy the same dollar.
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:
Purchasing Power Parity (PPP), which takes the inflation into account and calculates inflation differential.
Relative Economic Strength Approach, which considers the economic growth of countries to predict the direction of exchange rates.
Econometric model is another common technique used to forecast the exchange rates which is customizable according to the factors or attributes the forecaster thinks are important. There could be features like interest rate differential between two different countries, GDP growth rates, income growth rates, etc.
Time series model is purely dependent on the idea that past behavior and price patterns can be used to predict future price behavior.
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.
Sequence problems
Let us begin by talking about sequence problems. The simplest machine learning problem involving a sequence is a one to one problem.
One to One
In this case, we have one data input or tensor to the model and the model generates a prediction with the given input. Linear regression, classification, and even image classification with convolutional network fall into this category. We can extend this formulation to allow for the model to make use of the pass values of the input and the output.
It is known as the one to many problem. The one to many problem starts like the one to one problem where we have an input to the model and the model generates one output. However, the output of the model is now fed back to the model as a new input. The model now can generate a new output and we can continue like this indefinitely. You can now see why these are known as recurrent neural networks.
