I'm using TFT (Temporal Fusion Transformer) from Pytorch-forecating for the first time for my forecasting project. Im quite confused by a few things:
forecasted time series is very flat, what are the possible reasons for this? is it my train data set is too short? the train data set length is between 1 max_encoder_length and 2 times of max_encoder_length, ratio of max_encoder_length:max_prediction_length is 4:1;
how does optimize_hyperparameters work? will it update the model with best parameters after being run? or I should manually update the model with output values of the process? Im asking because after I run it, nothing seem happened, the model remain unchanged.
Related
I have to make a "behavioural model" upon a huge transactions dataset, able to return a "behavioral score" given to every new transaction of a user, comparing to the historical data of that user.
I am looking for advices on how I can run the DBSCAN with every user without manually setting every hyperparameter for each one (there are thousands).
My idea is:
extrapolate the transactions from a single user
since the datas are in 15 dimensions, I am putting MinPts = 30
i want to plot the distance of the K (= MinPts) neighbours curve and set eps = the point of maximum curvature of the curve .
The automation of the third point is giving me some difficulties. I have tried to use a library called KneeLocator, but did not give results.
I am looking for any suggestion
I have EEG data, for which I want to calculate the peaks' amplitudes and latencies. I'm working with MNE, and found the method get_peak in the Evoked object. However, I want to find peaks on epochs data (not averaged). How can I do it? I didn't see similar functions for the epochs object. I would prefer to do it through MNE, but other python libraries can also work. It's important that there is an option to get the amplitude and latency of the peaks, and choose a time window for detection.
In addition, I didn't understand if the get_peak returns only the highest peak, or something else? If there is more than one peak.
Thanks!
You can create an Evoked data structure from a single trial by just selecting a single trial from your Epochs structure and then applying .average(), e.g., as follows:
tmp_evoked = all_epochs[subj][cond][trial].average()
The above assumes you have an all_epochs object organized as trials within conditions within subjects (i.e., you're working at the group level). If your Epochs object has only one subject, then it would just be:
tmp_evoked = all_epochs[cond][trial].average()
You can further refine this to find the peak at only one channel as:
tmp_evoked = all_epochs[subj][cond][trial].pick(chan).average()
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
Is it possible to predict input data by providing output label on a trained model?
The question doesn't really make sense.
We use the terms input and output to describe the data w.r.t. the problem we are trying to solve.
For example, given a latitude on the earth's surface, it's possible to create a model to try to predict the average temperature in August. Here the input is the latitude and the output is the avg temperature. So, it is of course possible, to try to reverse this and instead frame the problem so as to try to train a model to predict the latitude of a place given it's avg temp in august. BUT in doing so, you have, by definition, changed the input and output around. Latitude is now the output and temp the input.
Input 5+2 gives Output 7
Input 6+1 gives Output 7
Input 4+3 gives Output 7.
As you see in the example above it is easy given two numbers and an operation to get a number uniquely. However starting from number 7 can you uniquely predict which were the two numbers that gave 7?
You may run into these sort of problems. However if it is one to one and onto function (bijective), then you might be able to reverse it. Off course you will have to reverse the labels of input and output.
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!