Table of Contents
Because you take only a very small fraction of the most recent, it allows to preserve much older values you saw very early in the average. See how good this looks when used to predict one-step ahead below. You see that it fits a perfect line that follows the True distribution and justified by the very low MSE. Practically speaking, you can't do much with just the stock market value of the next day. Personally what I'd like is not the exact stock market price for the next day, but would the stock market prices go up or down in the next 30 days.
Try to do this, and you will expose the incapability of the EMA method. You will now try to make predictions in windows say you predict the next 2 days window, instead of just the next day. Then you will realize how wrong EMA can go. Here is an example:. So no matter how many steps you predict in to the future, you'll keep getting the same answer for all the future prediction steps.
One solution you have that will output useful information is to look at momentum-based algorithms. They make predictions based on whether the past recent values were going up or going down not the exact values. For example, they will say the next day price is likely to be lower, if the prices have been dropping for the past days, which sounds reasonable. However, you will use a more complex model: an LSTM model. These models have taken the realm of time series prediction by storm, because they are so good at modelling time series data. You will see if there actually are patterns hidden in the data that you can exploit.
Long Short-Term Memory models are extremely powerful time-series models. They can predict an arbitrary number of steps into the future.
An LSTM module or cell has 5 essential components which allows it to model both long-term and short-term data. For a better more technical understanding about LSTMs you can refer to this article. You will be using that for your implementations.
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- Why Do You Need Time Series Models?!
- Overall, we have 72 other assets in the dataset — daily price for every asset..
You are first going to implement a data generator to train your model. This data generator will have a method called. Then each batch of input data will have a corresponding output batch of data. In this section, you'll define several hyperparameters. D is the dimensionality of the input. It's straightforward, as you take the previous stock price as the input and predict the next one, which should be 1. This denotes how many continuous time steps you consider for a single optimization step.
The larger the better. Batch size is how many data samples you consider in a single time step. You can see that there are three layers of LSTMs in this example. Next you define placeholders for training inputs and labels.
Evaluating Learner Performance
This is very straightforward as you have a list of input placeholders, where each placeholder contains a single batch of data. You will have a three layers of LSTMs and a linear regression layer, denoted by w and b , that takes the output of the last Long Short-Term Memory cell and output the prediction for the next time step.
Additionally, you can have the dropout implemented LSTM cells, as they improve performance and reduce overfitting. In this section, you first create TensorFlow variables c and h that will hold the cell state and the hidden state of the Long Short-Term Memory cell. You then calculate the LSTM outputs with the tf. Now, you'll calculate the loss. However, you should note that there is a unique characteristic when calculating the loss. For each batch of predictions and true outputs, you calculate the Mean Squared Error.
And you sum not average all these mean squared losses together. Finally, you define the optimizer you're going to use to optimize the neural network. In this case, you can use Adam, which is a very recent and well-performing optimizer. Here you define the prediction related TensorFlow operations. Finally you calculate the prediction with the tf. You should execute this operation at the start, every time you make a sequence of predictions. Here you will train and predict stock price movements for several epochs and see whether the predictions get better or worse over time.
Mean squared error - Wikipedia
You follow the following procedure. You can see how the MSE loss is going down with the amount of training. This is good sign that the model is learning something useful. The absolute error is calculated by adding the difference of all the predicted values from actual values of the label attribute, and dividing this sum by the total number of predictions. The prediction average is calculated by adding all the actual label values and dividing this sum by the total number of examples.
You can verify this from the results in the Results Workspace. Documentation 9. Description This operator should be used for performance evaluation of regression tasks only. Input labeled data This input port expects a labeled ExampleSet. It requires a Performance Vector. Output performance This port delivers a Performance Vector we call it output-performance-vector for now. If no main criterion is selected, the first criterion in the resulting performance vector will be assumed to be the main criterion.
The values of the label attribute are the actual values. Values of the label attribute are the actual values. Range: boolean correlation Returns the correlation coefficient between the label and prediction attributes. All the predicted values are added and the sum is divided by the total number of predictions.
Spearman's rho is a measure of the linear relationship between two variables.
Stock Market Predictions with LSTM in Python
The two variables in this case are the label and the prediction attribute. Kendall's tau is a measure of correlation, and so measures the strength of the relationship between two variables.
Fully qualified classname of the PerformanceComparator implementation is specified here. This parameter has no effect if no attribute has the weight role. WrappedModel and specify the desired performance measures. Typical performance measures for classification are the mean misclassification error mmce , accuracy acc or measures based on ROC analysis. For regression the mean of squared errors mse or mean of absolute errors mae are usually considered. For clustering tasks, measures such as the Dunn index dunn are provided, while for survival predictions, the Concordance Index cindex is supported, and for cost-sensitive predictions the misclassification penalty mcp and others.
It is also possible to access the time to train the learner timetrain , the time to compute the prediction timepredict and their sum timeboth as performance measures. To see which performance measures are implemented, have a look at the table of performance measures and the measures documentation page. If you want to implement an additional measure or include a measure with non-standard misclassification costs, see the section on creating custom measures.
The properties and requirements of the individual measures are shown in the table of performance measures. If you would like a list of available measures with certain properties or suitable for a certain learning Task use the function listMeasures. For convenience there exists a default measure for each type of learning problem, which is calculated if nothing else is specified.
As defaults we chose the most commonly used measures for the respective types, e. The help page of function getDefaultMeasure lists all defaults for all types of learning problems.
The function itself returns the default measure for a given task type, Task or Learner. In the following example we fit a gradient boosting machine gbm::gbm on a subset of the BostonHousing mlbench::BostonHousing data set and calculate the default measure mean squared error mse on the remaining observations. The following code computes the median of squared errors medse instead. Of course, we can also calculate multiple performance measures at once by simply passing a list of measures which can also include your own measure.