5
votes
Accepted
Is moving average(sliding window) a smoothing technique or forecasting technique?
A weighted average is what an ARIMA model is Seeking certain type of ARIMA explanation . It is the answer to ...the double question ...1) how many values should I include AND 2) how do I weight/...
- 30k
5
votes
In sliding window regression, what is the best way to select my training window and test set size?
Maybe I'm not understanding correctly but your test set size is all your data points (minus the initial training set) right? The size 1 is the forecasting horizon, how many data points forward you ...
- 1,266
4
votes
Accepted
Rolling vs Recursive vs Fixed Window Regression
Your definitions are correct.
Economic Forecastsing - Elliot and Timmerman (2016) is a good ref for that.
See the image therein:
However I'm still somewhat confused between multi-step ahead
...
- 5,779
3
votes
Accepted
ARIMA + Rolling Window
Using a rolling window is a very typical approach. Conceptually, they estimate the model every day using the last 500 days, so when a day is over, the next day they will update all the estimates based ...
- 1,478
3
votes
Out-of-sample Rolling window forecast with ARIMA(0,0,0) with non-zero mean
You asked "Is it a general thing that an ARIMA(0,0,0) becomes a MA(1) when the series is differenced?"
Yes if the differencing is unwarranted ... as in this example where Y(t) is a white noise series....
- 30k
3
votes
What is rolling mean and standard deviation in terms of stationarity?
Stationarity is a statistical property. As such, it is exact and always holding only in terms of the theoretical expected values through which we express it. For example, it is about having
$$E(X_t)=...
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3
votes
Accepted
Detrending using moving average
\begin{eqnarray}
\Delta^2(x_{t+1})&=&\Delta(\Delta(x_{t+1}))\\
&=&\Delta(x_{t+1}-x_t)\\
&=&\Delta(x_{t+1})-\Delta(x_t)\qquad\text{(by linearity of }\Delta \text{ operator)}\\
&...
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2
votes
Rolling sum of 2 sample KS test results
The KS test only has a limited number of possible p values, in particular for small sample sizes. Standard methods for combining p values, however, are based on the assumption that p values are ...
- 2,371
2
votes
Accepted
Rolling sum of 2 sample KS test results
So this one does not go unanswered I repeat here material from my comments. It is perfectly possible to combine the $p$-values using any of the standard methods. For the two values quoted in the ...
- 18.3k
2
votes
How to decide moving window size for time series prediction?
I would say that your first approach seems like a good start, it seems better to me than your second one. Your assessment of the possible risks, are correct as you could interpret this as tweaking ...
- 348
2
votes
Accepted
Impact of window size on estimated volatility using SMA or EWMA
If volatility was constant, then the impact would have been negligible. Unfortunately (?) volatility is time varying. If by SMA you mean simple moving average then the impact is that once the window ...
- 62.3k
2
votes
Moving Average window
Creating some aggregate (such as a moving average) of features as a new feature is a perfectly fine idea. I believe your question is perhaps not that much about 'convention' (there aren't any) but ...
- 81
2
votes
Root-mean-square error when having multiple prediction horizons
Averaging errors over different forecast horizons makes no sense in my opinion.
Suppose, you produced a set of dynamic forecasts $\hat y(t+h|I_t)$ where $t$ is the last actual observation used for a ...
- 62.3k
2
votes
Optimal window size for contextual outlier detection
I would need more information, but judging solely from your plot, it looks like a simple median filer (not average/mean) would do the job. If your outliers are single ticks, even a median filter of ...
- 233
2
votes
Accepted
Lag selection and model instability for ARIMA-GARCH in rolling windows for forecasting
I would also use auto.arima. The fact the the selected models change frequently between one window to the next may be due not only to frequent structural changes (...
- 69.5k
2
votes
Smoothing out target variable for spiky demand forecasting
I will be brutal: I think you are on the wrong track.
Your description of the problems with binning timestamped data is correct in principle. However:
The timestamps themselves are imprecise, so the ...
- 131k
1
vote
Accepted
Optimal window size for contextual outlier detection
Taking your description as a starting point, using ADWIN combined with kernel density estimation (KDE) could fit the bill.
The ADWIN algorithm automatically adjusts the window size given the level of ...
- 2,172
1
vote
Accepted
Root-mean-square error when having multiple prediction horizons
Hello while I can not show you my code, as it is confidential due to company legal agreement, I developed a Genetic Time Series fitting VAR in levels, VECM, and VAR in differences and doing a kmeans ...
- 1,658
1
vote
Accepted
Expected value of rolling variance/standard deviation of an AR(1) process
I will use vector and matrices rather than summations because this
makes the results easier to check e.g. using R. Let $\mathbf{x} :=
[x_1, \, \dots, \, x_n]^\top$ and $\boldsymbol{\varepsilon} :=
[\...
- 5,706
1
vote
Accepted
Does the method of reruning GARCH models every day (to update parameter values and improve out-of-sample forecasting performance) have a name?
I think this is simply known as expanding-window estimation. This can be contrasted to rolling-window estimation (also moving-window estimation) where you keep the window size fixed by discarding ...
- 69.5k
1
vote
How do we forecast using 3 point moving average?
Moving average does not have the "forecast" functionality, because this is a method for smoothing the time-series, not forecasting. If you wanted to use it for forecasting, the only way that ...
- 141k
1
vote
Efficient online (rolling window) estimation of a GARCH model
Werge & Wintenberger "An Adaptive Recursive Volatility Prediction Method" (2020) seem to be doing something close to what the OP is interested in. Here is the abstract:
The Quasi-...
- 69.5k
1
vote
Accepted
How does the sliding window work?
Well a sliding window of 24 would take 24 samples at a time moving down the index with one step. For example, if you had 24 hours in a window of length 24, you’ll have 0-23 samples in your window at ...
1
vote
How to compute the change of Ridge regression solution when one row of data changes?
Online Moving-Window Ridge Regression
You can use this same closed-form solution to update your $\beta$ online, even in a moving-window context. Suppose you have $m$ data points $x_1, \ldots, x_m$ ...
- 853
1
vote
Computation of multiple linear OLS regression with rolling window and/or update
The formulas may be completely correct but not so easy to extend. It'd be better to consider appropriate matrix algebra. This paper (especially the first 7pages) has all the formulas necessary for an ...
- 193
1
vote
Accepted
What is the relationship between average of a rolling intercept and the intercept from a regression over the entire period?
There is no direct relationship between these two estimation methods, and their interaction depends on the underlying data in a complex way. To see this, we can derive the formulae for the two ...
- 133k
1
vote
Ranking with multiple weights/ features
Let's begin by examining $a$ and $b$. $a$ is really a proportion of time, which runs from $0$ to $1$. If $a_{it} = 1$, then you have 1 (presumably important) entity in your time window. Likewise if $...
- 389
1
vote
Markov Switching GARCH - Expanding or Rolling window forecasting?
In the volatility forecasting literature most apply a rolling window approach. This is motivated by the literature on forecasting evaluation that mostly allows for fixed or rolling windows for the ...
- 1,155
1
vote
Moving window time series
Functio rollapply in package zoo will let you apply a function (given as argument FUN) to a ...
- 8,718
1
vote
Supervised learning: setting labels on sliding windows of sensor data
Your sliding window will generate something at every time period, assuming you slide it by one time slice at a time. So the feature you're generating from the sliding window will have a value at each ...
- 21.6k
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