All Questions
Tagged with moving-window time-series
25 questions with no upvoted or accepted answers
5
votes
0
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690
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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 ...
- 77
3
votes
0
answers
27
views
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, ...
- 131
3
votes
0
answers
300
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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 ...
- 31
3
votes
0
answers
2k
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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 ...
- 133
2
votes
1
answer
360
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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 ...
- 21
2
votes
0
answers
87
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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 ...
- 139
2
votes
2
answers
658
views
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 ...
- 21
2
votes
2
answers
1k
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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 ...
- 21
2
votes
0
answers
55
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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. ...
- 21
2
votes
0
answers
303
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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} - \...
- 559
1
vote
0
answers
57
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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 ...
- 11
1
vote
0
answers
48
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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 ...
- 342
1
vote
0
answers
32
views
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 $\mathbb{E}[X_t] = \text{const}, \forall t$? ...
- 11
1
vote
2
answers
802
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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.
...
- 19
1
vote
0
answers
50
views
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 ...
- 43
1
vote
1
answer
4k
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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 ...
1
vote
0
answers
350
views
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 (...
- 77
1
vote
0
answers
356
views
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 ...
- 1,334
1
vote
0
answers
748
views
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 ...
- 11
1
vote
0
answers
115
views
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 ...
- 141
1
vote
0
answers
531
views
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 ...
- 111
1
vote
0
answers
1k
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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 ...
- 11
0
votes
0
answers
70
views
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 ...
- 11
0
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0
answers
31
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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 ...
- 1,334
0
votes
0
answers
37
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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.,
...
- 602