Forecasts Machine Learning - machine-learning
This is a follow up question from my other question . So, I'm making an Machine Learning Model to forecast when some things happen. I will use softmax as output.
My question is, is it better to use 7 output nodes ( range from sunday - saturday, i.e. for data on monday, then the computer predict some things will happen in friday) or 0....n output nodes ( as in day interval since day h )?
If the weekday doesn't have to do something with your data, it's defenetly better to use the 0....n outputnodes since day n.
In that case, which differs from what you asked last time, a single neuron with relu as output might be even better. (This time the weekday seems not to play a role, so you try not to classify the weekday (classification - discrete), but want to know the time to the next event (regression - continuous), that also could be 3.54 days).
Classification: Softmax
Regression: Single Neuron with relu/linear/...
Related
Setting correct input for RNN
In a database there are time-series data with records: device - timestamp - temperature - min limit - max limit device - timestamp - temperature - min limit - max limit device - timestamp - temperature - min limit - max limit ... For every device there are 4 hours of time series data (with an interval of 5 minutes) before an alarm was raised and 4 hours of time series data (again with an interval of 5 minutes) that didn't raise any alarm. This graph describes better the representation of the data, for every device: I need to use RNN class in python for alarm prediction. We define alarm when the temperature goes below the min limit or above the max limit. After reading the official documentation from tensorflow here, i'm having troubles understanding how to set the input to the model. Should i normalise the data beforehand or something and if yes how? Also reading the answers here didn't help me as well to have a clear view on how to transform my data into an acceptable format for the RNN model. Any help on how the X and Y in model.fit should look like for my case? If you see any other issue regarding this problem feel free to comment it. PS. I have already setup python in docker with tensorflow, keras etc. in case this information helps.
You can begin with a snippet that you mention in the question. Any help on how the X and Y in model.fit should look like for my case? X should be a numpy matrix of shape [num samples, sequence length, D], where D is a number of values per timestamp. I suppose D=1 in your case, because you only pass temperature value. y should be a vector of target values (as in the snippet). Either binary (alarm/not_alarm), or continuous (e.g. max temperature deviation). In the latter case you'd need to change sigmoid activation for something else. Should i normalise the data beforehand Yes, it's essential to preprocess your raw data. I see 2 crucial things to do here: Normalise temperature values with min-max or standardization (wiki, sklearn preprocessing). Plus, I'd add a bit of smoothing. Drop some fraction of last timestamps from all of the time-series to avoid information leak. Finally, I'd say that this task is more complex than it seems to be. You might want to either find a good starter tutorial on time-series classification, or a course on machine learning in general. I believe you can find a better method than RNN.
Yes you should normalize your data. I would look at differencing by every day. Aka difference interval is 24hours / 5 minutes. You can also try and yearly difference but that depends on your choice in window size(remember RNNs dont do well with large windows). You may possibly want to use a log-transformation like the above user said but also this seems to be somewhat stationary so I could also see that not being needed. For your model.fit, you are technically training the equivelant of a language model, where you predict the next output. SO your inputs will be the preciding x values and preceding normalized y values of whatever window size you choose, and your target value will be the normalized output at a given time step t. Just so you know a 1-D Conv Net is good for classification but good call on the RNN because of the temporal aspect of temperature spikes. Once you have trained a model on the x values and normalized y values and can tell that it is actually learning (converging) then you can actually use the model.predict with the preciding x values and preciding normalized y values. Take the output and un-normalize it to get an actual temperature value or just keep the normalized value and feed it back into the model to get the time+2 prediction
Interpreting Seasonality in Time Series
I have a discrete time series covering 49 quarters between January 2007 and March 2019, which I am trying to analyse. Before undertaking various forms of analysis I wanted to check for the existence of seasonality and have tried to methods for such in R. In the first I used the WO function (Webel and Ollech) from the seastests package, which informed me that the data did not display seasonality. library(seastests) summary(wo(tt)) > summary(wo(tt)) Test used: WO Test statistic: 0 P-value: 0.8174965 0.5785041 0.2495668 The WO - test does not identify seasonality However, I wanted to check such again and used the decompose function, from which I got the below, which would appear to suggest a seasonal component. Can anyone advise if; I am reading the decomposed data correctly? AND Why there is such disagreement between decompose and the seastest results?
The decompose function is a simple function that basically estimates the (moving) period average. The volatility of your time series increases strongly in the last years. Thus the averages may pick up on some random increases. Also, the seasonal component that you obtain using the decompose() function will basically always look seasonal. set.seed(1234) x <- ts(rnorm(80), frequency=4) seastests::wo(x) plot(decompose(x)) Therefore, seasonality tests are preferable to assessing whether a time series really is seasonal. Still, if you have information that the data generating process has changed, you may want to use the test on the last few years of observations.
Learning from time-series data to predict time-series (not forecasting)
I have a number of datasets where each of them contains a number of input variables (lets say 3) as time series and an output variable, also as a time series and all over the same time period.
Each of these series has the same number of datapoints (say 1000*10 if 10 second data was gathered at 1000Hz).
I want to learn from this data and given a new dataset with 3 time serieses for input variables, I want to predict the time series for the output variable.
I will write the problem below in some non-English notation. I will avoid using terms like features, sample, target etc because since I haven't formulated the problem for any algorithm, I don't want to speculate what will be what.
Datasets to learn from look like this:
dataset1:{Inputs=(timSeries1,timSeries2,timSeries3), Output=(timSeriesOut)}
dataset2:{Inputs=(timSeries1,timSeries2,timSeries3), Output=(timSeriesOut)}
dataset3:{Inputs=(timSeries1,timSeries2,timSeries3), Output=(timSeriesOut)}
.
.
datasetn:{Inputs=(timSeries1,timSeries2,timSeries3), Output=(timSeriesOut)}
Now, given a new (timSeries1, timSeries2, timSeries3) I want to predict (timSeriesOut)
datasetPredict:{Inputs=(timeSeries1,timSeries2,timSeries3), Output = ?}
What technique should I use and how should the problem be formulated? Should I just break it as separate learning problem for each time stamp with three features and one target (either for that or next timestamp)?
Thank you all!
LSTM and labels
Lets start off with "I know ML cannot predict stock markets better than monkeys." But I just want to go through with it. My question is a theretical one. Say I have date, open, high, low, close as columns. So I guess I have 4 features, open, high, low, close. 'my_close' is going to be my label(answer) and I will use the 'close' 7 days from current row. Basically i shift the 'close' column up 7 rows and make it a new column called 'my_close'. LSTMs work on sequences. So say the sequence I set is 20 days. hence my shape will be (1000days of data, 20 day as a sequence, 3 features). The problem that is bothering me is should these 20 days or rows of data, have the exact same label? or can they have individual labels ? Or have i misunderstood the whole theory? Thanks guys.
In your case, You want to predict the current day's stock price using previous 7 days stock values. The way your building your inputs and outputs require some modification before feeding into the model. Your making mistake in understanding timesteps(in your sequences). Timesteps(sequences) in layman terms is the total number of inputs we will consider while predicting the output. In your case, it will be 7(not 20) as we will be using previous 7 days data to predict the current day's output. Your Input should be previous 7 days of info [F11,F12,F13],[F21,F22,F23],........,[F71,F72,F73] Fij in this, F represents the feature, i represents timestep and j represents feature number. and the output will be the stock price of the 8th day. Here your model will analyze previous 7 days inputs and predict the output. So to answer your question You will have a common label for previous 7 days input. I strongly recommend you to study a bit more on LSTM's.
How to predict several unlabelled attributes at once using WEKA and NaiveBayes?
I have a binary array that has 96 elements, it could look someting like this:
[false, true, true, false, true, true, false, false, false, true.....]
Each element represents a time interval in 15 minutes starting from 00.00. The first element is 00.15, the second is 00.30, the third 00.45 etc. The boolean tells whether a house has been occupied in that time interval.
I want to train a classifier, so that it can predict the rest of a day, when only some part of the day is known. Let's say I have observations for the past 100 days, and I only know the the first 20 elements of the current day.
How can I use classification to predict the rest of the day?
I tried creating a ARFF file that looks like this:
#RELATION OccupancyDetection
#ATTRIBUTE Slot1 {true, false}
#ATTRIBUTE Slot2 {true, false}
#ATTRIBUTE Slot3 {true, false}
...
#ATTRIBUTE Slot96 {true, false}
#DATA
false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,true,true,true,true,true,true,true,false,true,true,true,false,true,false,false,false,false,false,false,true,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false
false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,true,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,true,true,true,true,true,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,true,true,true,true,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false,false
.....
And did a Naive Bayes classification on it. The problem is, that the results only show the success of one attribute (the last one, for instance).
A "real" sample taken on a given day might look like this:
true,true,true,true,true,true,true,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?
How can i predict all the unlabelled attributes at once?
I made this based on the WekaManual-3-7-11, and it works, but only for a single attribute:
..
Instances unlabeled = DataSource.read("testWEKA1.arff");
unlabeled.setClassIndex(unlabeled.numAttributes() - 1);
// create copy
Instances labeled = new Instances(unlabeled);
// label instances
for (int i = 0; i < unlabeled.numInstances(); i++) {
double clsLabel = classifier.classifyInstance(unlabeled.instance(i));
labeled.instance(i).setClassValue(clsLabel);
DataSink.write("labeled.arff", labeled);
Sorry, but I don't believe that you can predict multiple attributes using Naive Bayes in Weka. What you could do as an alternative, if running Weka through Java code, is loop through all of the attributes that need to be filled. This could be done by building classifiers with n attributes and filling in the next blank until all of the missing data is entered. It also appears that what you have is time-based as well. Perhaps if the model was somewhat restructured, it may be able to all fit within a single model. For example, you could have attributes for prediction time, day of week and presence over the last few hours as well as attributes that describe historical presence in the house. It might be going over the top for your problem, but could also eliminate the need for multiple classifiers. Hope this Helps! Update! As per your request, I have taken a couple of minutes to think about the problem at hand. The thing about this time-based prediction is that you want to be able to predict the rest of the day, and the amount of data available for your classifier is dynamic depending on the time of day. This would mean that, given the current structure, you would need a classifier to predict values for each 15 minute time-slot, where earlier timeslots contain far less input data than the later timeslots. If it is possible, you could instead use a different approach where you could use an equal amount of historical information for each time slot and possibly share the same classifier for all cases. One possible set of information could be as outlined below: The Time Slot to be estimated The Day of Week The Previous hour or two of activity Other Activity for the previous 24 Hours Historical Information about the general timeslot If you obtain your information on a daily basis, it may be possible to quantify each of these factors and then us it to predict any time slot. Then, if you wanted to predict it for a whole day, you could keep feeding it the previous predictions until you have completed the predictions for the day. I have done a similar problem for predicting time of arrival based on similar factors (previous behavior, public holidays, day of week, etc.) and the estimates were usually reasonable, though as accurate as you could expect for human process.
I can't tell if there's something wrong with your arff file. However, here's one idea: you can add a NominalToBinary unsupervised-Attribute-filter to make sure that the attributes slot1-slot96 are recognized as binary.
There two frameworks which provide multi-label learning and work on top of WEKA: MULAN: http://mulan.sourceforge.net/ MEKA: http://meka.sourceforge.net/ I only tried MULAN and it works very good. To get the latest release you need to clone their git repository and build the project.