Questions tagged [unevenly-spaced-time-series]
Time series sampled or measured at unevenly (or irregularly) distributed time points.
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Predicting events - seizures in epilepsy. A question about time series models matching with observations
I've been keep a diary of epilepsy seizures, and would like to attempt prediction modelling as an help for better management of anti consultant therapy.
Could you help to suggest models that fit with ...
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19 views
Analyzing the relationship between language and poll data over time - R
I'm looking at linguistic markers (e.g. use of first-person plural pronouns “we,” “us,” “our,” “ours”) in political speeches of two candidates over time.
One hypothesis is that the use of those ...
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28 views
How to analyse simple internet downtime log?
I have a log of internet downtime (timestamp + downtime in seconds) and I'd like to analyse it to try and spot trends, especially if I can see that it normally happens around specific times of the day,...
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15 views
Standard error of mean for samples from a semi-regular time-series
I am quite a novice in statistics, but am currently facing a problem in my research where I would like to know how much I can trust the mean value from a set of 72 samples. Ideally, the approach would ...
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40 views
Cross Correlation between highly unevenly spaced time series
I have the following problem.
I have 2 time series, one on a yearly basis (2010 - 2020) and one on a daily basis (01/01/2010 - 31/12/2020).
I looking for methods how I can investigate the correlation ...
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20 views
Should you bin data when creating moving average to generate a forecast?
Let's say I have insurance claims that are created on a daily basis on Monday through Friday but sometimes there will be days in which we don't get a claim, perhaps it'll be a week or two that we don'...
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14 views
Time series methods that allow for intepretation of the coefficients of independent variables
For our project, we have to work with the social media text data of several hundred musicians. The data does not have equal time intervals, as musicians could send out messages twice a day or once a ...
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22 views
Equivalent of “lm” for irregular time series forecasting in R
I have a two column data frame corresponding to time series of the form (Date, Value). I want to predict future values of Value based on this data. I don't need anything fancy, just a quick and dirty ...
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35 views
Regression Discontinuity vs. Interrupted Time Series vs. Irregular (interrupted time series)?
I'm looking for the short term influence of wine ratings (the dummy variable rating) on wine prices (auction data) on 20 wines.
The data is heavily irregular and at some days more more bottles sold ...
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10 views
Estimating a transition matrix from irregular time series when time is not of interest
I have a dataset with the following variables:
id, representing person id;
state_nr, representing the state that someone is in;
...
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1answer
16 views
How to perform autoregression analysis with higher-resolution explanatory data
I have two time series of data, shown in the below plot. My response data is an annual total (brown, plotted in the middle of the year totalled), my explanatory data is a monthly summary index (black)....
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19 views
Fill missing values when aggregating a time series from minutes to hours
I am attempting to predict future values using AWS Forecast, and my data is by minute. However, due to data size constraints, I need to aggregate this data to hourly.
The problem is that I am missing ...
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15 views
Is multivariate state-space appropriate for looking at interactions/causality between three related time series?
I have three ecological time series which are related.
I want to look at if they can be used to predict each other and how exactly they relate to each other (ideally I want to see if one time series ...
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29 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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44 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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13 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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20 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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27 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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0answers
49 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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16 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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1answer
9 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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37 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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70 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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117 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
35 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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48 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 ...
2
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0answers
50 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 ...
2
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2answers
248 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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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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236 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'...
0
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1answer
56 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
204 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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0answers
25 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
130 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
110 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, ...
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2answers
611 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 ...
0
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1answer
19 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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0answers
91 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
183 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 ...
2
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1answer
514 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
310 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
204 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 ...
1
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1answer
63 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
117 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 ...
