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?
Related
I have count time series of demand data and some covariates like weather information every hour. I have used 168 hours (7 days) for encoder and 24 hours (next day) for decoder in DeepAR pytorch forecasting. E.G. using MTWTFSS for encoder to predict M (Monday)
After doing much testing I find that the 24 hours in prediction is more correlated with NOT the previous 7 days. It is more correlated with the same days in the past week. So I would need to use
MMMMMMM (mondays of previous weeks) to predict M (next monday).
Is it possible to tell TimeSeriesDataset object to train using this type of inputs?
I cannot manually create this like below
MMMMMMM TTTTTTTT WWWWWWWW…
because it will take any subsequence inside this time series like MMMMTTTT to use for encoder (MMMMTTT) and decoder (T). I do not want this. so is there a way to tell TimeSeriesDataset object to only sample the time series sequentially from the beginining without any overlaps during training? So that I can just feed the input time series as
MMMMMMM TTTTTTTT WWWWWWWW…
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.
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.
Updated question: This is a good resource: http://machinelearningmastery.com/understanding-stateful-lstm-recurrent-neural-networks-python-keras/
See the section on "LSTM State Within A Batch".
If I interpret this correctly, the author did not need to reshape the data as x,y,z (as he did in the preceding example); he just increased the batch size. So an LSTM cells hidden state (the one that gets passed from one time step to the next) started at row 0, and keeps getting updated until all rows in the batch have finished? is that right?
If that is correct then why does one ever need to have a time step greater than 1? Could I not just stack all my time-series rows in order, and feed them as a single batch?
Original question:
I'm getting myself into an absolute muddle trying to understand the correct way to shape my data for tensorflow, particularly around time_steps. Reading around has only confused me further, so I thought I'd cave in and ask.
I'm trying to model time series data in which the data at time t is a 5 columns in width (5 features , 1 label).
So then t-1 will also have another 5 features, and 1 label
Here is an example with 2 rows.
x=[1,1,1,1,1] y=[5]
x=[2,2,2,2,2] y=[15]
I've got an RNN model to work by feeding in a 1x1x5 matrix into my x variable. Which implies my 'time step' has a dimension of 1. However as with the above example, the second line I feed in is correlated to the first (15 = 5 +(2+2+2+2+20 in case you haven't spotted it)
So is the way I'm currently entering it correct? How does the time stamp dimension work?
Or should I be thinking of it as batch size, rows, cols in my head?
Either way can someone tell me what are the dimensions are I should be reshaping my input data to? For sake of argument assume I've split the data into batches of 1000. So within those 1000 rows I want a prediction for every row, but the RNN should be look to the row above it in my batch to figure out the answer.
x1=[1,1,1,1,1] y=[5]
x2=[2,2,2,2,2] y=[15]
...
etc.
I want to study how to perform LIBSVM for regression and I'm currently stuck in preparing my data. Currently I have this form of data in .csv and .xlsx format and I want to convert it into libsvm data format.
So far, I understand that the data should be in this format so that it can be used in LIBSVM:
Based on what I read, for regression, "label" is the target value which can be any real number.
I am doing a electric load prediction study. Can anyone tell me what it is? And finally, how should I organized my columns and rows?
The LIBSVM data format is given by:
<label> <index1>:<value1> <index2>:<value2> .........
As you can see, this forms a matrix [(IndexCount + 1) columns, LineCount rows]. More precisely a sparse matrix. If you specify a value for each index, you have a dense matrix, but if you only specify a few indices like <label> <5:value> <8:value>, only the indices 5 and 8 and of course label will have a custom value, all other values are set to 0. This is just for notational simplicity or to save space, since datasets can be huge.
For the meanig of the tags, I cite the ReadMe file:
<label> is the target value of the training data. For classification,
it should be an integer which identifies a class (multi-class
classification is supported). For regression, it's any real
number. For one-class SVM, it's not used so can be any number.
is an integer starting from 1, <value> is a real number. The indices
must be in an ascending order.
As you can see, the label is the data you want to predict. The index marks a feature of your data and its value. A feature is simply an indicator to associate or correlate your target value with, so a better prediction can be made.
Totally Fictional story time: Gabriel Luna (a totally fictional character) wants to predict his energy consumption for the next few days. He found out, that the outside temperature from the day before is a good indicator for that, so he selects Temperature with index 1 as feature. Important: Indices always start at one, zero can sometimes cause strange LIBSVM behaviour. Then, he surprisingly notices, that the day of the week (Monday to Sunday or 0 to 6) also affects his load, so he selects it as a second feature with index 2. A matrix row for LIBSVM now has the following format:
<myLoad_Value> <1:outsideTemperatureFromYesterday_Value> <2:dayOfTheWeek_Value>
Gabriel Luna (he is Batman at night) now captures these data over a few weeks, which could look something like this (load in kWh, temperature in °C, day as mentioned above):
0.72 1:25 2:0
0.65 1:21 2:1
0.68 2:29 2:2
...
Notice, that we could leave out 2:0, because of the sparse matrix format. This would be your training data to train a LIBSVM model. Then, we predict the load of tomorrow as follows. You know the temperature of today, let us say 23°C and today is Tuesday, which is 1, so tomorrow is 2. So, this is the line or vector to use with the model:
0 1:23 2:2
Here, you can set the <label> value arbitrarily. It will be overwritten with the predicted value. I hope this helps.