All Questions
Tagged with rolling-window or moving-window
108 questions
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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
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1
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44
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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 ...
- 11
3
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0
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27
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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, ...
- 131
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31
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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 ...
1
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1
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32
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How to interpret the differences in estimated variances?
I estimated the variance of Bitcoin in several ways using the var command in R, and within a GARCH model. I get series that look a bit similar, but the y-axis gives ...
0
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0
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70
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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 ...
- 11
0
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0
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28
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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 ...
- 557
1
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1
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96
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Smoothing out target variable for spiky demand forecasting
I am trying to predict ambulance demand for the next hour, for a city area in the USA, based on previous demand, weather, large people gatherings, and similar spatio-temporal factors - using Machine ...
- 61
3
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1
answer
151
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In sliding window regression, what is the best way to select my training window and test set size?
I am trying to forecast an index option's implied volatility using a sliding window regression and I'm a little confused on how I can go about cross validating with respect to the training and test ...
0
votes
1
answer
88
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Max of the running average of the kth through nth elements for a given probability distribution
This question is based slightly on https://www.reddit.com/r/AskStatistics/comments/16bqit0/calculating_probability_when_phacking_is_allowed/
Given a variable $X$, let $A_j$ be the average of $X_1$ ...
- 111
1
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1
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221
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rolling mean series - strong autocorrelation
I need to works with time-series of rolling means of a certain variable that comes from the data. I have this rolling means for N different individuals. Call them Y. I would like to test some ...
- 190
0
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1
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157
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Implementing Random Forest rolling window forecast in R [closed]
I want to forecast a dependent variable and I have some independent variables. First I shifted the dependent variable up, such that I have a supervised problem. For instance in January 2000 the ...
3
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0
answers
159
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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 ...
- 63
1
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0
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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
0
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2
answers
810
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Exact steps for rolling window CV evaluation or sliding window CV evaluation for SARIMA
So far I have using this process:
1)split data into training and test
2)do model selection(p,d,q, P,D,Q,etc) using training data(in this case, I used autoarima)
...
- 95
1
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1
answer
419
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Kernel Smoothing for Time Series data [closed]
I have generated a time series data set of measurements that are a bit noisy and I want to apply kernel smoothing to the data. My time series data is not regular however, meaning that the time ...
- 111
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0
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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
1
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0
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32
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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 $\mathbb{E}[X_t] = \text{const}, \forall t$? ...
- 11
1
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0
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229
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Rolling z-score or z-score? [closed]
Suppose that we have some time series data, in what context we use rolling z-score and when do we use z-score?
- 123
1
vote
1
answer
27
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Impact of religious holidays whose dates shift in the Gregorian calendar [closed]
I need to model and predict a quantitative variable that is heavily impacted by events, parties and celebrations. For I seek first to know the impact that has the religious festivals, the ramadan and ...
- 11
0
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1
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161
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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 ...
1
vote
2
answers
802
views
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
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0
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50
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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 ...
- 43
1
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2
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944
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Cross Validation for Time Series Classification (Not Forecasting!)
Is it possible to use regular k-fold cross validation where the folds contain entire time series in time series classification? I'm asking because most sources discussing cross validation with time ...
2
votes
1
answer
435
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Lag selection and model instability for ARIMA-GARCH in rolling windows for forecasting
I'm to produce rolling forecasts with an ARIMA-GARCH model using a moving window size of 1000. Given that structural changes in the series might take place at some point in the forecast horizon, is ...
- 33
1
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1
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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 ...
5
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0
answers
690
views
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
1
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0
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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
1
answer
554
views
ARIMA accuracy measures, rolling forecast
Regarding ARIMA model selection and especially accuracy measures several questions came into my mind. To shortly summarize, in my understanding, after necessary transformations/differencing, p and q ...
- 69
1
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1
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236
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Modifying tsCV function
I tried to modify the tsCV function to seperate between xreg_subset and xreg_future as Im going to use forecasted data for validate and test samples:
...
- 25
1
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0
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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 ...
- 1,334
2
votes
1
answer
4k
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Rolling vs Recursive vs Fixed Window Regression
What precisely are the differences between rolling, recursive and fixed window regression?
As far as I understand,
recursive: we train on a period $y(0)$ to $y(n)$ then predict $\hat{y}(n+1)$. Then ...
- 243
1
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0
answers
62
views
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 ...
- 21
2
votes
2
answers
902
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Optimal window size for contextual outlier detection
I am looking for methods to detect univariate contextual outliers in time series data. One example application is data from industrial plants in different (unknown) operation modes or slow trends or ...
- 153
0
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1
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528
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Rolling fixed window scheme for GARCH forecasting
I'm working on my bachelors thesis which mainly revolves around this paper: https://www.mdpi.com/2225-1146/4/1/3/htm
Shortly after describing the dataset in 3.1 the authors mention that they use a ...
- 5
3
votes
2
answers
3k
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Root-mean-square error when having multiple prediction horizons
I have a basic question about the root-mean-square error (RMSE). I have a prediction using an ARIMA model. I predicted a time series and use a rolling-horizon approach with overlapping or non-...
- 392
2
votes
1
answer
228
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Expected value of rolling variance/standard deviation of an AR(1) process
Consider a random variable following an AR(1) model:
$$x_t = \mu+\rho x_{t-1} + \epsilon_t$$
Assumme that $\epsilon_t$ follows $N(0,\sigma^2_\epsilon)$. Now consider the rolling variance and/or ...
- 139
2
votes
1
answer
360
views
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
3
votes
1
answer
1k
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Moving Average window
I am working with a multivariate time series problem which I try to predict 1 hour in the future. I am planning to use moving average of the features as separate new features. But I do not know if ...
- 679
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0
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34
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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 ...
- 101
2
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0
answers
87
views
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
0
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1
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150
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Does the method of reruning GARCH models every day (to update parameter values and improve out-of-sample forecasting performance) have a name?
It is my understanding that normally GARCH models make forecasts for say T-K days ahead. Instead of doing that I would like to use the data for days 1, 2, ...,k in my dataset to fit a GARCH model to ...
- 89
2
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2
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658
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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 ...
- 21
1
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0
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158
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Holt-Winters Multiplicative Alpha Beta Gamma [closed]
I need to create a table with Holt-Winters Alpha, Beta and Gamma (ABG)
I have the following code
...
- 11
0
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0
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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
3
votes
1
answer
3k
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Is moving average(sliding window) a smoothing technique or forecasting technique?
The rolling average method is mostly used to produce a smoothed series by removing noise. For ex- 3 window moving average, in general practice, the output for the fourth period is the 3 window moving ...
- 80
1
vote
2
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972
views
How do we forecast using 3 point moving average?
X<- c(3,6,8,10,6,5)
If I want to forecast using 3point moving average
I use ma(X,3) from forecast package
So this is going to give a series of smoothed average.
If I want to forecast further 2 ...
- 80
3
votes
1
answer
978
views
Efficient online (rolling window) estimation of a GARCH model
I have a time series $x_t$ of length $n$. I would like to model it using rolling window approach with window length (width) $w$:
window $1$: $x_1,\dots,x_w$,
window $2$: $x_2,\dots,x_{w+1}$,
$\dots$,
...
- 69.5k
1
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1
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703
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Out-of-sample Rolling window forecast with ARIMA(0,0,0) with non-zero mean
I am doing a rolling window out-of-sample forecast and have fitted an ARIMA(0,0,1) model to a first difference time series. People argue that sometimes simpler models are better than more complicated ...
- 99