Questions tagged [moving-window]
A window is a fixed-length subset of consecutive observations of a time series. The window is moved along the time series at a constant rate. AKA "rolling window".
46 questions with no upvoted or accepted answers
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Predict churn in a range of time after observation window is finished
I'm building a churn model. Each user's historic data (observation window) is a constant period, but each observation window contains different dates.
For example the next figure:
Let's say, that the ...
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Can you use a moving average as an instrumental variable?
I have panel data and am interested in changes in total expenditures. I would like to consider an instrumental variable approach to deal with an endogenous regressor – the short run elasticity of ...
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improve performance of finding rolling window maximum likelihood
There is data indexed by time:
$$
D_1, D_2, D_3, ..., D_T
$$
I have a model that I assume the parameter $\theta_t$ changes with time $t$.
As a result, I adapt a rolling window strategy:
$$
\theta_{...
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Determine a robust trend from noisy time series data, when start and end years have a material effect
I have about 20 years of data, each year has a number of observations. If I put a linear trend through the data, I get a trend, and this trend differs based on the the choice of start and end year, ...
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Fixed vs rolling forecasts: Empirical evidence
Many sources online recommend that we use a rolling window to make forecasts. As I am in a choice between using a fixed window and a rolling window for my data I am trying to find any empirical ...
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What are the potential problems with a rolling regression?
I have two time series that co-move, say a currency and a commodity, and the currency is highly impacted by the commodity, but also other factors. I'd like to determine when the currency becomes too ...
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Good book / resource for learning about moving window PCA?
I would like recommendations on textbooks (or online resources) covering MWPCA theoretically and with application examples (preferably in R, but also in Python, Mathematica, Matlab).
A brief intro:
...
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Difference between recursive and rolling window estimation
I am trying to check if my Auto Regressive Distributed Lag (ARDL) model provides stable estimates over time. I am not sure if I should be using a recursive or rolling window method. I know that the ...
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Correcting for multiple testing on non-independent sliding windows
Question
Values taken from adjecent windows in a sliding windows are corrolated. If I calculate a p-value from each window, how can I correct for the fact I have tested many windows, given that the ...
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2
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1
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Discovering peaks/patterns in time-series and clustering them
I have a dataset which contains minute level sensor measurements. Sample is shown here:
To me useful information are these peaks in time series, mostly their peak and duration. My idea is to take out ...
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How does choosing an even window size actually add a cyclical component to the model?
I am new to Time Series Analysis. Say, we have a time series $(y_{t})_{t}$ that we want to filter with a moving average filter. I have been told that we should choose the window size $L$ of the filter ...
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Rolling autocorrelation vs whole series autocorrelation
Suppose we have some financial time series. When we calculate the standard ACF, $\mu$ is considered as the average of all series' values. However, if we have a volatile series, the average can be ...
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Rolling Window Forecasting with ARIMAX while supplying actual values
I am comparing different exogenous variables in how good they support the forecast of the monthly seasonal adjusted unemployment rate. All my data is monthly (2006-01-01 until 2018-09-01) and ...
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2
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335
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DNN methodology and feature concatenation
I'm using someone else's job and I have a question that I cannot solve.
This work uses a DNN to match an electrical resistance to a bend angle. This is not very important, just for the context.
So, ...
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1
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Using a recoded window 10 days back in a GAM-model, is that a double whammy or is it OK to do?
I'm formulating a prediction in a logistic reression type in a GAM model.
...
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Picking the right prediction model
I am having a hard time trying to make/pick a prediction model in R.
The data: I have information on 40 different players, with all their recorded performances(training loads) over the last season. ...
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Use sliding window to find variance for seasonal time series in R
I would like to estimate the variance of a time series. Say, if the time series has a period of 24, and I want to estimate the variance using
$$ \sigma_t^2 = \frac{1}{2k+1} \sum^k_{-k} (y_{t+24k} - \...
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Comparing observed Vs predicted window overlap/intersection
I have a large number (~1.5 million) of protein sequences, each of them of different lengths.There are 6 schematic examples in the attached image.
Within each of these sequences, there are >= 0 ...
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A Sliding Goodness of Fit Method? (Foray into the Stats Community Wielding only R-Squared)
I am a bit of a Stats idiot.
I have two waveforms that I want to compare. One is an actual measurement, the other is a model of the first waveform which I calculate by convolving an impulse response ...
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Transformation w/ Rolling Regression (Residual Function)
In a time series with OLS regression curve $\widehat Y$ (rolling linear regression), and with $n=20,$ what can I say about this transformation? This formula is similar to a differential minus its ...
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Strange increasing in $R^2$ when MAE and RMSE worsened for OLS
I am currently working on my thesis, which involves using machine learning to predict non-stationary and seasonal time series. I am encountering some results that I cannot explain.
While I cannot go ...
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What is the probabilistic meaning of moving window statistics?
Moving window statistics (see this, for example) are sample statistics calculated over moving/rolling windows over a time-series.
For example, given the time-series $\{x_1,x_2,\dots\}$ one can ...
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A question about an estimation using moving average and moving standard deviation?
Given a time series data $\{X_t\}_{t = 0}^\infty$, what does its moving average and moving standard deviation estimate when there is no assumption that $\mathbb{E}[X_t] = \text{const}, \forall t$? ...
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2
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How to analyse results of classification for time series + sliding windows
Here is my context:
I have a time series composed of only 1 features. I want to be able to classify between two classes. To get more information out of these data, I am using a sliding time windows.
...
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Rolling autoregression coefficient
I was reading a paper and I saw that they run a 3-year rolling autoregression for 20 years (using for example 2013-2016 as trailing and 2016-2019 as forwarding) and got only one beta coefficient and ...
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1
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Difference between use cases of expanding and rolling window in backtesting
I was reading about different variants of backtesting in time series- expanding window & rolling window. I could find in texts about when to use which, but still I'm sort of unanswered.
Here's ...
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what is it an activity window in churn model?
I know that in a churn model many times you define an observation window (historic data) and a performance window (also dependent window, or response window).
I have read an article that the authors (...
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356
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Rolling forecast vs. static training data for financial timeseries?
I want to train a statistical model to predict financial asset returns.
I'm wondering whether it would be more effective to train a rolling forecast model rather than training a single model with a ...
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MSGARCH fitting doesn't work for the specific part of the data
I'm trying to do rolling (expanding) window forecast with MSGARCH and the model is failing for very specific $i$. For example I get error for $i=14$ and $i=30$. Data is snp500 returns data between ...
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Counting occurrences using n-grams
I was asked to do the following exercise:
Consider the sequence
IROIRDXMIRDOMORIORMTDMMDMWBIRQGDM
Count the number of occurences of IRDM using:
n-grams
...
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What is the difference between a discrete and continuous rolling window?
What I have read so far suggests that a discrete rolling window is say a yearly window, calculating somethig every year, which is from 01-01/31-12 and a continuous rolling window is a window that ...
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How to make predictions on sliding window?
I need help understanding how to construct sliding windows as well as how to perform final prediction. Any help is appreciated!
I have a dataset from sensing data with multiple features aggregated ...
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Average time series forecast errors from cross-validation with rolling origin
I'm calculating the MAPE and RMSE over a rolling origin cross-validation with fixed forecast interval for several models. For example, for a daily series with 3 years, I'm training my model with 2 ...
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How to perform windowframing for timeseries prediction
I am working on a market prediction project based on the timeseries data. Essentially i am trying to windowframe the data and predict whether the price goes up or down in an hour, 30 minutes and 15 ...
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Critique my method for predicting easter product sales from daily data
I hope this question benefits the site as well as myself. Thanks ahead for your time.
So I work for a company that sells (among a lot of things) personalized items specifically for Easter - stuffed ...
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How to use rasters in R to create a logit model?
I created land-use rasters for the city of Montreal for 2012, 2014, and 2016; all of which I converted from vectors to rasters in QGIS. I want to create a logit model to predict land-use changes. I ...
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How to handle/preprocess time dependent features in a neural network
I want to use a neural network to model a biological continuous variable. This variable depends on a bunch of events that happened in the preceding hours, sometimes up to 24 hours, including the ...
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Sliding window with labelled time series of sensor data
I'm using a sliding window to obtain features (like mean, varianve etc) of a labelled time series of sensor readings. The goal is to train a binary classifier (like linear regression or SVM) to detect ...
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Autoregession meets multiple regression? - Help with verbiage and approach
Needing some help with verbiage and opinions on how I am approaching this model.
I have counts of people over the past 24 months.
month
count
1
100
2
105
...
...
24
200
First, I reverse the ...
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Data leakage in time series forecasting framed as a supervised learning problem
Suppose that I have a simple univariate time series. My goal is to use the value of 3 consecutive days to predict the value of the fourth day.
I built my dataset by applying a rolling window that ...
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Stationarity and moving standard deviation
Suppose $\{X_t\}$ is stationary process. We observe a sample of $N$ observations from the process, i.e., $x_1, x_2, ..., x_N$. The stationarity property implies that the distribution doesn't change ...
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What is it called when an outlier falls out of a rolling window statistical calculation?
I have a time series $X_t \sim N(0, 1)$. There is a single outlier at index 347, at 8.5 standard deviations from the mean. If I now compute a rolling window standard deviation of $X_t$ with window ...
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Calculate the daily standard deviation for time series (stock market) in R
I´m modeling with diffrent GARCH-Models the daily standard deviation of a stock market. That includes a rolling forecast model of the daily standard deviation. This works pretty well so far.
To ...
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Is there a way to deal with spiking growth rates due to small sample numbers?
I'm looking at plotting county coronavirus case density growth rates (moving 7 day window) and am finding that when cases first appear the new case growth rate is very large due to the fact that there ...
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How to reflect more global patterns in timeseries?
I have some signal data of a robot recorded in every minute each day.
e.g.,
...
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433
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Rolling window AR(2)-GARCH(1,1) VaR intuition query
I have an AR(2)-GARCH(1,1) model and I need to use a 1-day rolling window 1 -step ahead forecasts to calculate the 5% conditional VaR.
I know how to calculate the VaR for 1-step ahead only - I ...
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