I want to train a Multivariate LSTM model by using data from 2 datasets MIMIC-1.0 and MIMIC-3. The problem is that the vital signs recorded in the first data set is minute by minute while in MIMIC-III the data is recorded hourly. There is a interval difference between recording of data in both data sets.
I want to predict diagnosis from the vital signs by giving streams/sequences of vital signs to my model every 5 minutes. How can I merge both data sets for my model?
You need to be able to find a common field using which you can do a merge. For e.g. patient_ids or it's like. You can do the same with ICU episode identifiers. It's a been a while since I've worked on the MIMIC dataset to recall exactly what those fields were.
Dataset
Granularity
Subsampling for 5-minutely
MIMIC-I
Minutely
Subsample every 5th reading
MIMIC-III
Hourly
Interpolate the 10 5-minutely readings between each pair of consecutive hourly readings
The interpolation method you choose to get the between hour readings could be as simple as forward-filling the last value. If the readings are more volatile, a more complex method may be appropriate.
Related
I have table with different datatypes. Some of my columns are :
name, time, date, number_of_files, hour_works, type_of_job
Jack, 10:24:54, 2015-02-15, 82, 20, project manager
….etc
I want to train these features to predict type_of_job in the company by using a randomforest model.
My question is should I convert the columns to specific datatypes to get good accuracy and what about time and data? I have around 48970 rows and this is first time I work with machine learning.
Yes, it is necessary to convert the data. Usually all the columns should have numeric format:
you can extract features from time - day, hour, week and so;
type of job is a categorical feature, common transformation methods are labelencoding and onehotencoding;
the same could be done with other categorical columns, like name;
if you use linear model, then numerical features should be normalized;
Maybe this is a silly question but I didn't find much about it when I google it.
I have a dataset and I use it for regression but a normal regression with FFNN didn't worked so I thought why not try an LSTM since my data is time dependent I think because it was token from a vehicle while driving so the data is monotonic and maybe I can use LSTM in this Case to do a regression to predict a continuous value (if this doesn't make sense please tell me).
Now the first step is to prepare my data for using LSTM, since I ll predict the future I think my target(Ground truth or labels) should be shifted to the up, am I right?
So if I have a pandas dataframe where each row hold the features and the target(at the end of the row), I assume that the features should stay where they are and the target would be shifted it one step up so that the features in the first row will correspond to the target of the second row (am I wrong).
This way the LSTM will be able to predict the future value from those features.
I didn't find much about this in the internet so please can you provide me how can I do this with some Code?
I also know what I can use pandas.DataFrame.shift to shift a dataset but the last value will hold a NaN I think! how to deal with this? it would be great if you show me some examples or code.
We might need a bit more information regarding the data you are using. Also, I would suggest starting with a more simple recurrent neural network before you start going for LSTMs. The way these networks work is by you feeding the first bit of information, then the next bit of information, then the next bit etc. Let's say that when you feed the first bit of information in, it occurs at time t, then the second bit of information is fed at time t+1 ... etc. up until time t+n.
You can have the neural network output a value at each time step (so a value is outputted at time t, t+1... t+n after each respective input has been fed in). This is a many-to-many network. Or you can have the neural network output a value after all inputs have been provided (i.e. the value is outputted at time t+n). This is called a many-to-one network. What you need is dependednt on your use-case.
For example, say you were recording vehicle behaviour every 100ms and after 10 seconds (i.e. the 100th time step), you wanted to predict the likelihood that the driver was under the influence of alcohol. In this case, you would use a many-to-one network where you put in subsequent vehicle behaviour recordings at subsequent time steps (the first recording at time t, then the next recording at time t+1 etc.) and then the final timestep has the probability value outputted.
If you want a value outputted after every time step, you use a many-to-many design. It's also possible to output a value every k timesteps.
The example in the link below has a training and validation set from time series data. There is no mention of a test set. Why isn't there one and what would it entail to have one for a dataset whose time series data is being generated on the fly in real time?
I have 3hrs of data collected at 1s interval. I would like to predict the next 30 min before it becomes available. What should be the train/validate/test split look like? Can test set be skipped?
https://www.tensorflow.org/tutorials/structured_data/time_series
It is never recommended to skip the test set. In the TensorFlow example, the purpose was to demonstrate how you can play with time series; you can test on the 'test set' just like you do with your validation, with the constraint that the test set is completely unknown: here we come to your second question.
With regard to the test set, in your use case, like you said, the test set is the data generated on the fly.
You can, of course, split your initial dataset into train/val/test. But the second test set which evidently coincides with your model 'live deployment' would be to predict on 'on-the-fly-generated-dataset' => this means you would feed the data real-time to your model.
The train-val-test split depends on how you want to create your model: how many time-steps you want to use(how many seconds to take into account when prediction the next step etc, how many variables you are trying to predict, how many time-steps ahead you want to predict(in your case 30 minutes would be 30*60 = 1800, since your dataset signals frequency is in seconds). It's a very broad question and refers more on how to create a dataset for time series analysis for multi-step prediction.
I have data for hundreds of devices(pardon me, I am not specifying much detail about device and data recorded for devices). For each device, data is recorded per hour basis.
Data recorded are of 25 dimensions.
I have few prediction tasks
time series forecasting
where I am using LSTM. As because I have hundreds of devices, and each device is a time series(multivariate data), so all total my data is a Multiple time series with multivariate data.
To deal with multiple time series - my first approach is to concatenate data one after another and treat them as one time series (it can be both uni variate or multi variate) and apply LSTM and train my LSTM model.
But by this above approach(by concatenating time series data), actually I am loosing my time property of my data, so I need a better approach.
Please suggest some ideas, or blog posts.
Kindly don't confuse with Multiple time series with Multi variate time series data.
You may consider a One-fits-all model or Seq2Seq as e.g. this Google paper suggests. The approach works as follows:
Let us assume that you wanna make a 1-day ahead forecast (24 values) and you are using last 7 days (7 * 24 = 168 values) as input.
In time series analysis data is time dependent, such that you need a validation strategy that considers this time dependence, e.g. by rolling forecast approach. Separate hold-out data for testing your final trained model.
In the first step you will generate out of your many time series 168 + 24 slices (see the Google paper for an image). The x input will have length 168 and the y input 24. Use all of your generated slices for training the LSTM/GRU network and finally do prediction on your hold-out set.
Good papers on this issue:
Foundations of Sequence-to-Sequence Modeling for Time Series
Deep and Confident Prediction for Time Series at Uber
more
Kaggle Winning Solution
Kaggle Web Traffic Time Series Forecasting
List is not comprehensive, but you can use it as a starting point.
I am preparing time series data to build an RNN model (LSTM). The data is collected from sensors installed in a mechanical plant. Consider I have data for input and output temperature of a compressor along with the time stamps.
Like this there is data for around 20 parameters recorded along with their time stamps. Problem is there is a difference in the time stamps at which data is collected.
So how do I ideally match the time stamps to create a single dataframe with all the parameters and a single time stamp?
Since an RNN doesn't know anything about time deltas but only about time steps, you will need to quantify / interpolate your data.
Find the smallest time delta Δt in all of your series
Resample all of your 20 series to Δt/2* or smaller (Nyquist-Theorem)
* Actually you'd need to do a Fourier transform and then use twice the cutoff frequency as sampling rate. Δt/2 might IMHO be a good approximation.