The error to update the weights for LMS is given by:
e(n) = d(n) - y(n)
Assuming e as the error, d as the desired signal and y as the estimated output at given time step n.
How do we know the value of d(n) if we are using it real-time?
In a supervised setting, we would have samples for the desired output but how do we achieve it for real-time adaptive filtering?
Related
Thank you for reading. I'm not good at English.
I am wondering how to predict and get future time series data after model training. I would like to get the values after N steps.
I wonder if the time series data has been properly learned and predicted.
How i do this right get the following (next) value?
I want to get the next value using like model.predict or etc
I have x_test and x_test[-1] == t, so the meaning of the next value is t+1, t+2, .... t+n,
In this example I want to get predictions of the next t+1, t+2 ... t+n
First
I tried using stock index data
inputs = total_data[len(total_data) - forecast - look_back:]
inputs = scaler.transform(inputs)
X_test = []
for i in range(look_back, inputs.shape[0]):
X_test.append(inputs[i - look_back:i])
X_test = np.array(X_test)
predicted = model.predict(X_test)
but the result is like below
The results from X_test[-20:] and the following 20 predictions looks like same.
I'm wondering if it's the correct train and predicted value.
I'm wondering if it was a right training and predict.
full source
The method I tried first did not work correctly.
Seconds
I realized something is wrong, I tried using another official data
So, I used the time series in the Tensorflow tutorial to practice predicting the model.
a = y_val[-look_back:]
for i in range(N-step prediction): # predict a new value n times.
tmp = model.predict(a.reshape(-1, look_back, num_feature)) # predicted value
a = a[1:] # remove first
a = np.append(a, tmp) # insert predicted value
The results were predicted in a linear regression shape very differently from the real data.
Output a linear regression that is independent of the real data:
full source (After the 25th line is my code.)
I'm really very curious what is a standard method of predicting next values of a stock market.
Thank you for reading the long question. I seek advice about your priceless opinion.
Q : "How can I find a standard method of predicting next values of a stock market...?"
First - salutes to C64 practitioner!
Next, let me say, there is no standard method - there cannot be ( one ).
Principally - let me draw from your field of a shared experience - one can easily predict the near future flow of laminar fluids ( a technically "working" market instrument - is a model A, for which one can derive a better or worse predictive tool )
That will never work, however, for turbulent states of the fluids ( just read the complexity of the attempts to formulate the many-dimensional high-order PDE for a turbulence ( and it still just approximates the turbulence ) ) -- and this is the fundamentally "working" market ( after some expected fundamental factor was released ( read NFP or CPI ) or some flash-news was announced in the news - ( read a Swiss release of currency-bonding of CHF to some USD parity or Cyprus one time state tax on all speculative deposits ... the financial Big Bangs follow ... )
So, please, do not expect one, the less any simple, model for reasonably precise predictions, working for both the laminar and turbulent fluidics - the real world is for sure way more complex than this :o)
In Gelman book, the effective number is defined in terms of the following;
R hat
between- within MCMC sequence of variance, B and W
the number of MCMC samples, denoted by n
the number of chains, denoted by m
I do not know how the samplig() calculate the between MCMC sequence of variance for the case chains=1. So, I cannot calculate these terms ( B,W,m). I want to implement some algorithm according to the paper:https://arxiv.org/abs/1804.06788.
Roughly speaking, this paper construct some test statistics which is uniformly distributed under the null hypothesis that the MCMC sampling is correct. And if MCMC sampling is not correct, then the histogram of the test statistics become skew shape and this deviation from uniformity tells us the MCMC contains bias. I want to implement but it needs to calculate the above quantities.
In rstan, is there such function to extract the above quantities ? I think the process of calculation of R hat statistics, the above quantities B,W, m are retained in some place in the stanfit S4 object.
I am sorry, I found n_eff, but I do not know the choice of m of the case chains =1.
In the case that only one chain is estimated (which should not be happening anyway), then m = 2 because the post-warmup draws from the single chain are split into the first half and the second half. This splitting method is discussed in the documentation.
My main problem is how should I pre-process my dataset that is basically a 60 minutely sequenced numbers inputs that will result in a 1 hourly output. Knowing that each input vector every minute is producing some output, but unfortunately this output can't be observed until 1 hour is passed.
I thought about considering putting 60 inputs as one big input vector which corresponds to 1 hourly output on a normal ML classfier, hence having 1 sample at a time. But I don't think it would be time series anymore.
How can I represent that to be doable in an LSTM environment?
I am having problems not on understanding the k means algorithm but on how to apply it on training ,validation and testing data.Is it like this:
Training phase: Apply k-means on the input data and then we get centroid value (in my case three).For each centroid value assign a label say 1,2,3.Suppose in training phase I input sixty such samples .So in total i get 60*3 centroids each with label 1,2,3..
Testing phase:Apply k means on the input signal.We get centroids.Compare this with centroid obtained from training phase centroids.Which ever is closest to it assign the same label for it?
k-means does not have a "training" and a "testing" phase. It is an unsupervised algorithm.
At most, it is only applied to testing data.
Do not approach it like a classificator. It is not a classification algorithm.
The objective of k-means is:
Split my input data set into k convex partitions, such that the sum of all squared deviations in all dimensions from the mean of each partition is smallest.
There are no labels.
I've trained a simple logistic regression model in SSAS, using Gender and NIC as discrete input nodes (NIC is 0 for non-smoker, 1 for smoker) with Score (0-100) as a continuous output node.
I want to predict the score based on a new participant's values for Gender and NIC. Of course, I can run a singleton query in DMX; for example, the following produces a value of 49.51....
SELECT Predict(Score)
FROM [MyModel]
NATURAL PREDICTION JOIN
(SELECT 'M' AS Gender, '1' AS NIC) as t
But instead of using DMX, I want to create a formula from the model in order to calculate scores while "disconnected" from SSAS.
Investigating the model, I have the following information in the NODE_DISTRIBUTION of the output node:
ATTRIBUTE_NAME ATTRIBUTE_VALUE SUPPORT PROBABILITY VARIANCE VALUETYPE
Gender:F 0.459923854 0 0 0 7 (Coefficient)
Gender:M 0.273306289 0 0 0 7 (Coefficient)
Nic:0 -0.282281195 0 0 0 7 (Coefficient)
Nic:1 -0.802106901 0 0 0 7 (Coefficient)
0.013983007 0 0 0.647513829 7 (Coefficient)
Score 75.03691517 0 0 0 3 (Continuous
Plugging these coefficients into a logistic regression formula -- that I am being disallowed from uploading as a new user :) -- for the smoking male example above,
f(...) = 1 / (1 + exp(0 - (0.0139830071136734 -- Constant(?)
+ 0 * 0.459923853918008 -- Gender:F = 0
+ 1 * 0.273306289390897 -- Gender:M = 1
+ 1 * -0.802106900621717 -- Nic:1 = 1
+ 0 * -0.282281195489355))) -- Nic:0 = 0
results in a value of 0.374.... But how do I "map" this value back to the score distribution of 0-100? In other words, how do I extend the equation above to produce the same value that the DMX singleton query does? I'm assuming it will require the stdev and mean of my Score distribution, but I'm stuck on exactly how to use those values. I'm also unsure whether I'm using the ATTRIBUTE_VALUE in the fifth row correctly as the constant.
Any help you can provide will be appreciated!
I'm no expert, but it sounds to me you don't want to use logistic regression at all. You want to train a linear regression. You currently have a logistic regression model, these are typically used for binary classification, not continuous values, i.e., 0-100.
How to do linear regression in SAS
Wikipedia: linear regression
more details: the question really depends, like most datamining/machine learing problems, on your data. If your data is bimodal, more than 90% of the training set is very close to either 1 or 100, then a logistic regression MIGHT be used. The equation used in logistic regression is specifically designed to render YES/NO answers. It is technically a continuous function, therefore results such as .34 are possible, but they are statistically very unlikely (in typical usage you would round down to 0).
However, if your data is normally distributed (most of nature is) the better method is linear regression. Only problem is it CAN predict outside of your range 0-100, if given a particularly bad data point. In this case you would be best off rounding (clipping the result to 0-100) or ignore the data point as an outlier.
In the case of gender, a quick hack would be to map male to 0 and female to 1, then treat gender as an input for the model.
SSAS linear regression
You do not want to be using logistic regression if you are trying to model a score restricted to an interval [0,100]. Logistic regression is used to model either binary data or proportions based on a binomial distribution. Assuming a logit link function what you are actually modelling with logistic regression is a function of probability (log of odds) and as such the entire process is geared to give you values in the interval [0,1]. To try to use this to map to a score does not seem to be the right type of analysis at all.
In addition I cannot see how regular linear regression will help you either as your fitted model will be capable of generating values way outside of your target interval [0,100] and if you are having to perform ad hoc truncation of values to this range then can you really be sure that your data has any effective meaning??
I would like to be able to point you to the type of analysis that you require but I have not encountered this type of analysis. My advice to you would be to abandon the logistic regression approach and consider joining the ALLSTAT mailing list used by professional statisticians and mathematicians and asking for advice there. Or something similar.