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".
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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 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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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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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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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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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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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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Supervised learning: setting labels on sliding windows of sensor data
Suppose that I have a set of accelerometer data collected with one sensor and one label for each measured data point. These labels describe different states of my system e.g., $state_A, state_B, ...
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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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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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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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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 ...
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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.
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First, I reverse the ...
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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 ...
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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 ...
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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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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 ...
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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 ...
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I'm getting "jumpy" loadings in rollapply PCA in R. Can I fix it?
I have 10 years of daily returns data for 28 different currencies. I wish to extract the first principal component, but rather than operate PCA on the whole 10 years, I want to rollapply a 2 year ...
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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 $X_1$ ...
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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 ...
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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 ...
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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)
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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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Rolling forecasts: training versus forecast accuracy evaluation
Questions:
Are rolling forecast examples (like the ones below) only useful for evaluating a model's accuracy, or can a rolling forecast be used to train a model?
Are models trained using a rolling ...
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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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Forecast evaluation for rolling forecast [closed]
I have rolling forecast for each month. I would like to do some forecast evaluation. How do I do this?
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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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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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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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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?
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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 ...
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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 ...
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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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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 ...
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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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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 ...
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Rolling sum of 2 sample KS test results
In order to compare two lists of samples from 'before-treatment' and 'after-treatment', I am doing a two sample KS test using the ks_2samp function from Python's scipy.stats package which gives me the ...
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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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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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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 ...
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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 $2$: $x_2,\dots,x_{w+1}$,
$\dots$,
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How to decide moving window size for time series prediction?
I have a model to predict +1 day ahead of this time series.
Looking at the chart you can notice some seasonality every 5 days. I suspect using a moving window as training set could help me making a ...
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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:
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Computation of multiple linear OLS regression with rolling window and/or update
How can I efficiently calculate an OLS fit for N multiple variables for a rolling window?
I've worked out how to do it for 1 and 2 variable linear fits, I'd like to extend to the general case of N ...
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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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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 ...
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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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Tuning an exponential moving average to a moving window mean?
The alpha parameter of an exponential moving average defines the smoothing that the average applies to a time series. In a similar way, the window size of a moving window mean also defines the ...
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