Questions tagged [unevenly-spaced-time-series]
Time series sampled or measured at unevenly (or irregularly) distributed time points.
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24 views
Real time time series prediction with different input frequencies
I want to make predictions about an event occurring at the certain time every day. To be specific, I am thinking of one of the train X arriving at the station Y (e.g. at 3.30pm every day). My aim is ...
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24 views
Can I use Granger causality for missing data/ irregular time series?
I have two time series and I want to look at their Granger causality. My data is from two irregular time series. Irregular here means sampling was done sporadically across 3 years. In some months, ...
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11 views
Time Series Classification with Unequal Time Between Events and Number
I have a problem where I'd like to predict whether a customer is going to convert. I have event data for these customers over time and the histogram for time to conversion follows a 1/x pattern where ...
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10 views
One-sequence Two-period crossover with repeated measures
I am struggling a bit with determining how to properly specify a model. I have multiple repeated measures on individuals prior to some exposure, and then multiple repeated measures on individuals ...
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25 views
Time dependent representation for time series events with different time gaps?
In natural language processing, we can treat characters as evenly spaced time series in RNN models where time gaps are independent of the sequence and only sequential positions matter.
If I want to ...
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12 views
Statistical Test for Trends in Samples from a Possibly Changing Distribution
I have some data of quantitative measurements of the language in books from the years 1800 to 2000. For each year I have between 0 and 50 samples, mostly about 10 samples per year. If I bin the data ...
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8 views
How to handle missing values when implementing Autoregressive House Price Index with Repeat Sales
I would like to implement a House Price Index using the autoregressive repeat-sales method as in Nagaraja et al. (2011) but can't find code for an implemented example. I specifically don't know how ...
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7 views
Predicting Multiple Values Values Using Time Series Forecasting
I want to illustrate my question with the following example:
I have a wholesale company through which I sell 200 products:
P1,P2,P3 .... P200
to a 1000 customers ...
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28 views
Calculating EWMA & EWMV of concurrency from duration & interval
I'm looking to calculate the exponential moving average & exponential moving variance of a continuous series of request/responses, using each response's duration and the interval (time delta) ...
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15 views
General steps in dealing with time series classifiers which are very very long and have gaps
I am working on a time series classification problem based on real world data. The data has gaps and the time series are over long periods of time thus there is quite a lot of it. I don't want to ...
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7 views
Creating an index using transactional data
I have a dataset of a series of leases.
Variables we have: precinct, date (in quarters), lettable area, price as well as a few others.
These assets are not homogenous, although are comparable.
In some ...
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30 views
Is it appropriate to interpolate a signal for frequency analysis?
From an experiment, I have (somewhat) irregularly sampled data.
The aim is to find the dominant frequency of the signal.
As I understand it, most methods for frequency analysis require evenly spaced ...
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48 views
Why is Multiple Imputation with Chained Equations (aka FCS) unfeasible in longitudinal data with irregular time intervals?
In the simulation study of Huque et al. (2018), they compare different multiple imputation methods in a longitudinal data setting. They mention a specific shortcoming of the standard default methods ...
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113 views
What is the best method to predict the water consumption of EACH customer for the next month?
Say we have a dataset that has the following attributes:
customer_id: There are a total of 1000 customers, each of them with a unique customer_id
observation_date: The date on which we last observed ...
2
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0answers
32 views
Does it make more logical sense to model the discrete FFT output as a categorical variable or a numerical variable?
I am training a time-series data classifier and some of my features are the output of CT FFT. The results are of course discrete frequencies. I understand that they are in numerical order and higher ...
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45 views
What model for highly irregular time series and very few observations?
I would like to apply an ARIMA model to some data to forecast values into the future. It has to do with bank disbursements. The problem is that, for a normal project, you may have up to 8 observations ...
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0answers
40 views
Spot unusual patterns in a discrete intermittent time series
I have a multitude of daily time series representing the volume of a certain product arriving per day at a station. There are as many time series as their are stations, and they each look like the ...
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2answers
178 views
Data leakage in temporally overlapping train-test split
Question: In the sliding window train-test split strategy, will there be data leakage if, say, I train on a dataset $X_{t}$ to predict values $y_t$ that were collected after my test data $X_{t+1}$?
...
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18 views
Similarity index between 2 unevenly sampled time series
Say I have 2 time series $s_1$ and $s_2$ with independent variable $x_1$, and dependent value $y$. These 2 series are not evenly sampled across $x_1$, or even sampled at the same rate.
Now, I have ...
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27 views
Forecasting with Irregularly Missing Data
Suppose I am supplying $N = 1000$ vendors, and I am looking for a way to predict their demand for my product over $T = 90$ days. Concretely, I hope to take some features for each vendor, such as their ...
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148 views
Event-based time-series analysis
I'm trying to find patterns in a game event that happens randomly over time. The data looks like this:
...
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52 views
Linear Mixed Effects Models - Time dependent covariates with irregular sampling
I've been looking at using a linear mixed effects model to describe the change in a biomarker $y$ over time in a large number of people over a time period $T$ (measured until an event happens). I want ...
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27 views
Equalize multiple unevenly spaced time series for forecasting
I am building a time-series forecasting model to predict some patterns in climatological data.
The dataset consists of many (2 mln) time series which look for example as:
However the observations ...
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106 views
Comparing time series data with different interval
I have time series data for one day (temperature) measured every 5 seconds and would like to increase the period between measurements and somehow calculate the level of error/similarity/correlation (I'...
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1answer
38 views
How to detect a drop in regularity / increase in spontaneity of time data?
I have been tasked with detecting changes in regularity for multiple datasets. Each dataset is linked to a different type of event, and each dataset consists of a few hundred timestamps representing ...
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2answers
117 views
Correlation between two time series of very different frequency?
I am wondering if there is a limit to calculation of the correlation between two vectors symbolizing events of VERY different frequency.
A = events that can appear in the millisecond area
B = events ...
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23 views
Customer next visit behavior forecast
I'm currently working with retail data about a store and the goal is to predict when each customer will visit the store again e.g customer id = 1 will probably visit again in 6 days(recency)
My ...
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1answer
91 views
Scale of time covariate in `corCAR1()` matters?
I'm running a bunch of GAM analyses on time series data (measurements of my own weight). Unlike many examples I see online, my data is not spaced evenly in time. In fact, time points can come minutes ...
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0answers
86 views
Multivariate time series analysis with different sampling rates?
I have four time series that cover the same time period. I want to perform cointegration analysis on them to investigate any potential cointegration relations and to estimate a VECM model. However, ...
3
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2answers
388 views
How to forecast number of event occurences in a certain time period?
I am attempting to predict how many times a certain event will happen in a time period. For instance, predict the number of time the event will occur in the next 5 hours. I have data going back a ...
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1answer
18 views
Mean time to event with uneven follow up times
I'm trying to compare the effect of two treatments on the time to fracture healing, and came across a study that does the following:
They compared effect of treatment A vs treatment B on fracture ...
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0answers
19 views
Between Group comparison for parameters measured on different time points
I have a dataset of 100 patients for whom data was recorded of 3 different parameters say Heart rate, temperature and ETCO2 levels which are measured in 3 phases eg. Phase 0, Phase 1 and Phase 2. The ...
0
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1answer
27 views
Tackling challenging semi-weekly or weekly data with gaps
I am working on ferry itineraries (i.e. tickets sold in a specific itinerary). Consider the scenarios:
1) itinerary taken only on Tuesdays and Thursdays every week ( but with gaps - depending on ...
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82 views
Missing as opposed to non existent data in time-series forecasting
Suppose you have a set of observations that occur at regular intervals in time, but containing regular gaps during which there is no data, not because it is hidden or missing, but because the ...
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0answers
155 views
Handling non-uniform frequency data
I have some medical data (heart rates) at non-uniform intervals (usually readings every few min at the start of the study and several readings a minute toward the end). The timing and when the change ...
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1answer
420 views
Notation for unevenly-spaced to evenly-spaced time series conversion
I have an unevenly-spaced time series. To make it evenly-spaced, I resample the time series to a larger timespan (e.g. day) and sum up all values within this time frame.
In Python (and pandas) it is ...
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1answer
257 views
How to deal with unevenly spaced time data in time series linear regression
I have a dataset that has a number of instances that look like the following:
...
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1answer
153 views
Practical handling of Dynamic Time Warping for time-series with unequal sampling frequency (irregular time series)
Hi all and thank you for taking time to read this.
When I read the general literature about dynamical time warping (and the vignette for the $\texttt{R}$ package $\texttt{dtw}$), the time series seem ...
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0answers
24 views
Representing Multiple Variables Measured at Multiple Independent Times
I'm attempting to find a good storage/data representation in "R" for a data set with a large number of variables, each of which is measured independently for a number of years but at irregular ...
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1answer
58 views
How to calculate correlation for 2 time series where the tick times do not line up
I'm looking to compute intraday correlation for 2 tick by tick intraday commodity price time series.
The tick times do not line up across the 2 time series. I'm thinking of converting the time ...
1
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1answer
106 views
How to check if a data is poisson sampled?
I was reading one article which develops a theory for the Poisson sampled data. That is the data is collected over time-points $\{T_k, k>1\}$, which are jump-moments of a homogeneous Poisson ...
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1answer
286 views
How to model time series with unevenly spaced observations?
I have three time series of the following form:
$T = T_{2000}, T_{2004}, T_{2008}, …$
$U = U_{1998}, U_{1999}, U_{2000}, U_{2001}, U_{2002}, U_{2003}, …$
$V = V_{1998}, V_{1999}, V_{2000}, V_{2001},...
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43 views
Classify time series with unequal lenghts [closed]
I have a set of time series sensor measurements (acceleration and gyroscope readings) for driving events (harsh acceleration, harsh brake …) with the type, start and end of each event.
I need to ...
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0answers
130 views
Why ARMA models works on assumption of uniformly sampled time series?
I am dealing with irregularly spaced time series data. I have been reading papers how ARMA based approaches can be used for time series forecasting.
However, ARMA assumes uniformly sampled time ...
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0answers
71 views
Smoothing with non-regular observations
If we have data that changes continuously in time, and we sample this data at regular intervals - i.e. we get samples $x_0$, $x_1$, $\dots$, where the time $\Delta t$ between taking samples $x_i$ and $...
5
votes
3answers
819 views
Binary target prediction using LSTM with sparse events in time
I have a data of patients that have multiple events happening in there medical history, I'd like to predict a target of having a specific targeted-event in the next 30 days.
The data is timestamped ...
2
votes
1answer
431 views
R: generalised additive model on proportional data
Introduction
I am analysing temporal population data on the amphipod Orchestia gammarellus. At several moments each year, all animals were collected from a small spot, and several life history traits ...
2
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1answer
102 views
Machine Learning techniques for uneven series of events
I don't know the correct terminology and so long everything I typed into google lead me to some version of time series modeling where all time series had the same number of points in a given time ...
3
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2answers
58 views
Example of dataset where the data collected at time-points $g(t_1), g(t_2), \ldots$
What would be some practical scenarios where we collect data at time-points $g(t_1), \ldots, g(t_n)$, where $g$ is an increasing function? For example, $g(t) = \exp(t)$ or $\ln t$.
To be more clear, ...
0
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1answer
1k views
LSTM - random and always-different time between data measurements?
I am working with a time series problem where the time between two data measurements is random, and I am trying, without luck, to find an LSTM architecture that can handle this.
A very simplified ...
