Questions tagged [unevenly-spaced-time-series]
Time series sampled or measured at unevenly (or irregularly) distributed time points.
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Aggregating physical science data from multiple sources for time series
I’m attempting to build a time series model using water quality data. The problem is, for any given site where this data exists, it is irregular time intervals and sparse data (anywhere from 20-70% ...
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Multi-variate multistep time series Forecasting for non-stationary data
The problem is that I have a very special time series. It is sensors data for a machine. I have about 400 sensor data which I want to use to forecast the machine advancing speed.
The data contain a ...
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GAM model parameter estimation problems
I have been trying to develop a GAM to predict future subsidence from previous subsidence data. I am new to GAM and have based my code on [this].1 The data looks like this:
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Can you use Dynamic Factor Analysis with time-series datasets that cover different time periods?
I have time series data for several sites and I'm interested in looking at whether the changes in covariates at these sites vary in a consistent manner over the time series. I thought dynamic factor ...
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Find significant metrics in a multivariate time series
I have collected statistics after running several benchmarks on a mobile processor. I've exported them to R and my data frames are like the following example.
There are multiple metrics (each one ...
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Daily data shows seasonality when averaged across month. How to adjust on a daily basis?
I have match data across multiple years. I've collected the data in the form Date Total Points Scored
Now here are some issues:
(i) since this is match data, it ...
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How to deal with share change variables in a regression where the time periods are different?
The dependent variable in my regression is a share change (subgroup/population). In some years the change is across one year and in other years the change is across two years. Is it OK to halve the ...
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Mixed model for repeated measures with different timepoints and intervals
is there a way to design a mixed model for uneven number of measurements per subject and (more importantly) with uneven time intervals between measurements which are taken at different time points (...
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Why do simulated arrival times from a Poisson distribution seem to show periodicity?
I am experimenting with simulated arrival times drawn from a Poisson distribution. To construct the arrival times, I am randomly drawing inter-arrival times from the inverse CDF, which is ...
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How to weigh time-series data with a utility function with jumps?
I have data measured at fixed intervals in one process. It needs to be weighed with cost value that is generated in another process that is analogue and the costs it generates have jumps.
Imagine a ...
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Can rescaled range analysis to calculate Hurst exponent be used in unevenly sampled data?
I am working with some data that I'd like to calculate the Hurst exponent of using a rescaled range analysis. The only issue is, this data I am working with is sampled unevenly. The timesteps aren't ...
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Can I use OLS techniques to analyse trends in documents from irregularly spaced time intervals
Apologies in advance as this is a lengthy query. I have a few questions involving research looking at the differences in level of ambition of language of documents on environmental issues from Group ...
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How to incorporate known future target values in prediction model?
Imagine that we collect time series data from a sensor. Due to measurement errors and let's say power outages, there are gaps in the time series that can be quite long. For example if data would be ...
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When do ARMA models fail?
I have just started learning about Autoregressive–moving-average model (ARMA). On the Wiki page, it has been mentioned that:
ARMA is appropriate when a system is a function of a series of
unobserved ...
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Generating a surrogate time series from unevenly spaced time series
I work in the atmospheric sciences and I have data from an automated weather station measuring mean sea level pressure. Due to an extreme event(and consequent loss of electricity)the measurement is ...
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Hypothesis testing on a moving-average model on unevenly-spaced time series
I have some irregular time series $X$, where $X_t$ are identically distributed, and bounded between 0 and 1. I perform a moving average $Y_t = \sum_{t'} \frac{w(t-t')}{\sum_k w(t-k)}X_{t'}$. The ...
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What is this type of data called?
An event occurs once per period, such as once per year. Time is measured in discrete units, such as days of the year. Let $A_y$ be the day in year $y$ on which this event occurs. However, we do not ...
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Predicting the dates of intermittent events
Hello I am trying to predict the date and location of certain events using an LSTM model. The observations of dates are not structured (sometimes no events for many days and sometimes many events on ...
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Lagged predictors in irregular-time / asynchronous / time-unconstrained data
In growth curve modeling or other approaches, when time is constrained/synchronous/regular (i.e. panel/wave data; all observations occur synchronously), lagged prediction is trivial - simply add t-1 ...
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Extract features of uneven time series and use it in cross-sectional study
I have a data like below
subj_1 = 10,20,15,30,60,70,90 (in resolution of years - 2011 to 2018)
subj_2 = 10,20,30,40 (in resolution of months - Jan 2011 to Apr 2011)
subj_3 = 10,15,20,30,45,55,60,70,90,...
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How can I generate probabilistic forecasts to do probabilistic classification?
I have a collection of univariate, irregularly spaced, financial time series. Each series is labeled by its class. The image below shows some example data.
A note on the data:
The time series could ...
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Controlling for time elapsed between baseline and treatment
I'm having a mixed model with 5 repeated measurements (time is categorical because the timepoints represent discrete events).
Edit: I have 5 meaningful timepoints: t0 baseline (neutral timepoint); t1 ...
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Short Time Series Granularity of Timestamps
I have to make a prediction on a time series whereby there are 10 years of data. My first option is to predict the values aggregated to the annual level using SARIMAX (excluding the seasonality ...
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Interrupted Time Series with Unevenly Distributed Samples
I'm working on causal inference using Interrupted Time Series Design. I have multiple samples per day and am selecting my analysis bandwidth based on pre-treatment RMSE on leave-on-out cross ...
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Forecasting Prices with interdependence that form a Timeseires
I have already asked a simmilar question, but i thoguth that this was not phrased well and hence i am trying a new post were i ask a better question. Let me know if this is ok. Judging by some of the ...
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Unevenly spaced multiple time series modelling
In the context of unevenly spaced (multiple) time series (USTS), are there any classical approaches? If they were evenly spaced, we would try ARIMA, or VAR, or even State-Space models.
I've been ...
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causal effects when y is continuous and discontinuous over time
I am interested in estimating the causal effects of A(treatment) on Y(outcome). Here treatment is a binary variables (yes vs no). Outcome is a continuous variable(weight), and this is normalized to Z ...
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Regression for hierarchical time series
I have a dataset with time series A, B and C from different countries. These time series have different starting points, missing values, and irregular intervals (only weekdays).
Instead of forecasting,...
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How to get daily returns from irregularly spaced price time-series?
I have a timeseries that has irregularly spaced time indices as below
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Feature creation based on irregular time series with strictly monotonic values
I am given the following problem to run predictive maintenance on the following mess:
I have some IoT sensors out in the field, but I cannot easily change the software on them. Also, data collection ...
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Unevenly spaced time-series forecasting and anomaly detection for an industrial usecase
I am currently working on a PhD project for a car manufacturing company, which basically consists of creating a predictive maintenance application for the machines that are currently used to fill the ...
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How to create an ML training dataset from unevenly spaced multivariate timeseries?
I have a time series dataset with multiple features X_n from which I want to predict an output y. However, both the x and y values are unevenly spaced and were sometimes collected at different ...
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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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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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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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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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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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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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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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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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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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Measuring frequency of change in time series data with inconsistent time intervals?
Context
I'm tracking auction house prices for items in Guild Wars 2 in a database. They provide an API that allows me to get the item's current buy/sell offer quantities and prices at that moment. As ...
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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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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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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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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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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 ...
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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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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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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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