Questions tagged [time-series]
Time series are data observed over time (either in continuous time or at discrete time periods).
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Training multiple keras models and combining outputs to determine losses
I'm trying to predict the future states of a 1D travelling wave (square, triangle and sawtooth) using a deep learning setup in Keras. The waves are discretised in a 1024 data points. As this gives a ...
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1answer
12 views
LSTM : multi-step multidimensional multivariate multi-site timeseries forecasting
I'm working on a project in which i'm trying to do a pollution forecasting.
I googled around and found that LSTM is a good candidate for this task, however, I'm still struggling at how to adapt it to ...
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1answer
12 views
Voting classifier using grid search for Time Series
I have three models
Arima
Auto ARIMA
Double Exponential Smoothing
I want to apply ensemble method - voting method and allow the classifier to learn weights for these three models.
I have checked ...
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7 views
Time Series Prediction for non-uniformly varying dependent variables
I have a dataset with the following properties:
DATETIME: range from "01.01.2014 01:00:00" to "12.31.2016 23:00:00"
(index)
Units: Category (#53) Technology: Category (#5)
Capacity: Continuous value ...
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1answer
7 views
How to get the formulas used by seasonal_decompose for Trend and Seasonality
I'm trying to use decomposition to forecast into the future. From my reading I understand that I can do this by adding a trend formula to a seasonality formula. I know that I can decompose a time ...
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1answer
33 views
How to optimally train deep learning model using output as new input
I'm trying to train a network to predict the future. My current setup uses 5 time steps as inputs from the past, each consisting of 10 features, resulting in a [5, 10] input matrix (initially ...
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8 views
What is the largest public wearable accelerometer dataset?
I'm looking for public datasets of people wearing a device with an accelerometer (and potentially other sensors eg gyroscope or magnetometer). What are some of the largest available datasets like ...
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1answer
11 views
How to train a model to predict a time window than an event will occur on a website
I have a list of historical timestamps of when a specific event occurred on a website. Currently the timestamp represents a 30 minute window that the event happened within. I am looking to train a ...
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14 views
Import the same interval of previous week into the deep model
In a dataset, the data are the average of vehicles speed in the points (cells) of a map. I am trying to build a prediction model.
While the inputs are the average of vehicles speed of all points in ...
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11 views
TimeDistributed Layers vs. ConvLSTM-2D
Could anyone explains for me the differences between Time-Distributed Layers (from Keras Wrapper) and ConvLSTM-2D (Convolutional LSTM), for purposes, usage, etc.?
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1answer
34 views
Keras - How to classify 1D time series
Say I have a training dataset composed by 128 1-Dimensional time series in form of numpy arrays.
They all correspond to a certain action that I label action_1 and ...
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7 views
Representation based algorithms for clustering work for multivariate time series?
https://www.sciencedirect.com/science/article/abs/pii/S0031320305001305
and https://www.sciencedirect.com/science/article/abs/pii/S0306437915000733.
These papers explain how clustering is made for ...
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1answer
13 views
Supervised learning for timeseries classification problem
I'm trying to use a supervised classification algorithm on a timeseries problem and my model is performing to well i think.
It's a time to failure problem.
I have 1000 sensors and have to predict if ...
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0answers
16 views
Time series: how to examine variance, stationarity, graphs?
I'm working with time-series and bank shares. I have difficulty analyzing graphs to find trends or stationarity in the data. I would like to apply the differentiation (first or second) to the data to ...
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17 views
Forecasting time series of binary vectors of given length
Please help me. I have a time series
$$
x^{(1)}, x^{(2)}, \ldots, x^{(n)}
$$
where $x^{(i)}=(x_1^{(i)}, x_2^{(i)}, \ldots, x_m^{(i)})$, $x_j^{(i)} \in \{0,1\}$,
that is every $x^{(i)}$ is a binary ...
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1answer
25 views
how to find holiday effect on revenue?
I have 2 datasets from 2013-2017 for each day.
a) Revenue generated by Locations and date. b) Holiday name and date
I would like to know how each holiday is impacting the revenue by location. I am ...
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0answers
22 views
Input explanatory categorical variables along with time series into neural network
I want an advise on the ways to enter time series along with additional variables into convolutional neural network.
Story first: I have a dataset of time series with daily energy consumption data (...
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1answer
31 views
LSTM regression bias increases when targets go close to 0
I've build a LSTM model for time series forecasting. Results are not bad, with a mean normalized error of 7%. However, this normalized bias shows a clear pattern: The closer to 0 the value to predict, ...
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0answers
20 views
Support Vector Machines for Time Series Forecasting [on hold]
I have dataset that has Temporal features as Day of week, Hour, Minute, Second, isWeekend and a target value (Y) that represent Number of requests
I want to use SVR to predict Y. How can I do this ...
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0answers
10 views
Suitable algorithm for forecasting demand from mixed seasonal/non-seasonal data
I am new to data science and have an interesting forecasting challenge.
Suppose I am a publisher of text books and related study materials (mock exam papers, revision notes and so on) for different ...
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0answers
15 views
Layman's explanation of when to use which smoother algorithm/technique: FFT, loess, Savitzky-Golay, etc
As an analytics practitioner, I frequently come across noisy data (e.g. IoT data). When building a model or machine learning algorithm, it can be advantageous to smooth this data. Over the years, I ...
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5 views
Which model can solve the “sequence demand”'problem?
I have a regression problem. When a truck comes, it influences the demand of employees for the next 30 days. Additionally the demand depends on the type of truck (when the truck is big, we need more ...
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11 views
Time-series analysis: Validity of Dickey-Fuller test with missings in data?
I have a huge (univariate) time-series dataset (100 MB), imported from Excel into Python. Column A contains a timestamp (over a 2-second window) and column B contains the value that was measured in ...
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0answers
21 views
On-line Time Series to Time Series Transformation
I'm a novice to ML, and I'm investigating methods for real-time transformations on time-series data. By "On-line" and "real-time", I mostly mean that, in it's application, all the data is not ...
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1answer
21 views
Using classification of previous sample in neural networks
I am trying to classify the state of a machine using different features coming from a set of sensors.
I am treating the problem like a time series, so I windowed the stream of the sensors each X ...
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15 views
Input shape for simpler time series in LSTM+CNN
Following is code from https://machinelearningmastery.com/how-to-develop-rnn-models-for-human-activity-recognition-time-series-classification/ which uses LSTM and CNN with TimeDistributed for human ...
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1answer
42 views
Binary classification model with time series as variables
This is probably a simple question. Assume I'm interested in modelling a binary variable, with various covariates, including ones that are time series observations. In the usual modelling approach, ...
1
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1answer
38 views
How to apply Stacking cross validation for time-series data?
Normally stacking algorithm uses K-fold cross validation technique to predict oof validation that used for level 2 prediction.
In case of time-series data (say stock movement prediction), K-fold ...
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2answers
37 views
How to learn from time series with multiple values for each time points
A multivariate time-serie has more than one time-dependent variable and it is my case.
Still for each time I have not one entrie of dependent variables but many entries, like:
...
1
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1answer
18 views
How to find similarity of two series over time containing periodic trends?
Considering the data is received from a streaming source each second.How to distinguish if both the line graphs 'look' same/different in real time, statically, like the picture given below
Edit:
1....
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2answers
38 views
Branch of data science that covers event based time series?
Let's say I have discrete events in time, e.g. patients getting sick, and I want to predict whether theses events are indicators of some other underlying event, e.g. a disease outbreak. Usually, one ...
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22 views
Detect Missing Records in Dataset
I have a dataset that contains several measures from various widgets on a daily basis. While the widgets remain relatively stable over time, sometimes there are legitimate reasons for one to disappear ...
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0answers
8 views
Regression based on series of depth (similar to time series)
I have a data with set of independent variables and a target variable. The target variable is exponentially distributed based on the depth.
Is there a way to identify a general depth function for my ...
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0answers
30 views
Using SMAPE as a loss function for an LSTM
I am currently working on a time series forecasting problem and am looking into using an LSTM.
My final accuracy metric that I use to determine whether or not the forecast is good or not is defined ...
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0answers
30 views
Keras TimeSeries - Regression with negative values
I am trying to make regression tasks for time series, my data is like the below, i make window size of 10, and input feature as below, and target is the 5th column. as you see it has data of {70, 110, ...
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0answers
15 views
How to model machine learning problem for cache replacement policy?
I am trying to implement machine learning on Cache Replacement Policy. I want to train a ML model on labelled data acquire from Belady's Optimal Algorithm for Cache replacement policy.
For example,
...
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2answers
37 views
How to model & predict user activity/presence time in a website
I need to make a prediction model based on some historical data from a website's user login system. Suppose my dataset has some features like user login time and logout time for each day for a ...
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0answers
24 views
Can an LSTM learn correlations between time series and produce skillful predictions for individual time series?
I am trying to build a model that is capable of producing a multi-step forecast for many different time series. To keep the example simple, let's say I have three different time series, $T_1$, $T_2$ ...
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0answers
11 views
How to compute server times from logs that have only mean response times?
I have a log where each entry has a timestamp, a number of requests that were processed since the last timestamp, and the mean response time for those requests. Assuming one CPU core and one infinite ...
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1answer
27 views
How to Identify p (lag order) for ARIMA Model in Python
here is my auto correlation plot.
Generated by the following python code.
...
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1answer
26 views
Sequences of time series data with only 1 output classification
So I'm facing a problem where I have a sequence (30h of data with 10sec intervals) and which is labeled to a class (3 different classes) for the whole sequence. I'm used to work with time series who ...
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0answers
12 views
How to tune parameters for Time Series Analysis, when forecasting is only dominated by one feature and error is not getting reduced?
I am trying to predict time series based on 150 features. When I plot correlation of these features, I am getting 20 features with more or less importance but every model I use, it is completely ...
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1answer
40 views
Keras multi-label time-series classification considering time-series as an input image vector
I am trying to build a multi-class classifier using Keras. I am not quite sure I have implemented it correctly.
Data is like this
label time-series variables [0:25728}
...
1
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1answer
32 views
How do you detect seasonality(multiplicative or additive) in a time series data?
Trying to apply seasonal_decompose on timeseries data. It looks something like this:
...
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0answers
15 views
How to prepare data for time series RNN
I want to use RNN on my data.
The data is from a number of medical devices, it is a time series. My problem is that there are few types of files, each having data from a deferent source:
File #1: ...
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1answer
20 views
Input sequence ordering for LSTM network
When training a LSTM network with time series data, I guess the order in which this data is fed matters, my question is how should this ordering be...
Let's take a time-series vector which will be ...
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1answer
26 views
Prediction: plugin a corelation table (neuron) into a Time-Series Neuron in Keras/ TF
i am adding more details
I have a time series of Babies (1,2,3) showing how many problem they have each week (Born week 1 to week 80) and in which organ (14 organ).
There is a separate numeric time-...
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1answer
23 views
Is it better to use a MinMax or a Log Return normalization to predict stock price movements?
I am trying to use a LSTM model to predict d+2 and d+3 closing prices. I am not sure whether I should normalize the data
with a MixMax scaler (-1,+1)
using the log return
(P(n)-P(0))/P(0) for each ...
0
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1answer
17 views
how to calculate number of datapoints within a given time interval?
I have a dataframe with one column which is a timestamp. I have converted that column to a datetime object using pandas to_datetime() method. However what I want is to ...
3
votes
1answer
71 views
Regex-style pattern-matching for time series
This is more of a "what technology/library would you use for this?" question than anything else.
I have categorical time series data, and need to match cases in these time series to known patterns. ...
