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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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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Which modelling method to use for this time series data?

I have a time series data of reaction times (RTs) from a psychophysics study. The RTs are influenced by whether a stimulus is presented to the left or right (binary predictor). Although the RTs are ...
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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 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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43 views

Walk Forward validation window for time series with redraw

I'm looking to perform walk forward validation on my time-series data. Extensive document exists on how to perform rolling window: or expanding window But this validation does not correspond ...
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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 ...
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Moving window multiple linear regression

I would like to perform a multiple linear regression on a time-series in order to find the variables having the most influence on the output. The dataset is quite small: 21 times 16 windows.The 16 ...
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21 views

Brief explanation of sliding window Pearson's correlation?

I have come across a few research articles I'm trying to understand that use sliding window Pearson's correlation. However, in my research I have yet to come across a basic explanation of what problem ...
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34 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 ...
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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 ...
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33 views

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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41 views

Time series analysis and cross-validation with slide-window

I m trying to understand the time series analysis and cross-validation with slide-window. My question is inspired to this other question on Cross Validated but it wants go deep in this argument with ...
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225 views

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 ...
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59 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 ...
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249 views

Multivariate Garch DCC-ROLL in R (RMGARCH)

Little Disclaimer I originally posted this on Stack Overflow, but I'm not sure which is the correct place, because this question demands a knowledge of Econometrics. So, I'll replicate here and if I'...
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82 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 $2$: $x_2,\dots,x_{w+1}$, $\dots$, ...
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153 views

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 ...
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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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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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87 views

What is the relationship between average of a rolling intercept and the intercept from a regression over the entire period?

If i calculate rolling (e.g. 3 periods back) intercepts for a time series using OLS, is the average of these rolling intercepts then in some way related to the intercept from an OLS of the entire ...
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Predictions in time series

I am working on project about the kitchen sink method and the lasso. My objective is to make in the sample predictions by, of course, taking part of the database as historical data and the other part ...
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22 views

Ranking with multiple weights/ features

We have entities where every entity has start ($s_i$) and end ($e_i$) times and count $c_i$. An entity is important if its interval ($e_i - s_i$) is large and if its $c_i$ is large. Here's what I ...
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155 views

Deciding Rolling mean and standard-deviation Window of a Time-Series Distribution

I have a Daily time-series data of 6 years. And I need to do Weekly forecasting on this Daily-basis data. Before forecasting, I want to find rolling mean & STD to determine the stationarity of ...
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458 views

ARIMA + Rolling Window

I'm currently working on building an ARIMA+GARCH model using R. My dataset consists of the logarithmic returns of the Dow Jones index for a period of 11 years 2005-2016, however, it's worth noting ...
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How to estimate policy impact using interrupted time series, when outcome variable is a moving average?

Suppose you want to evaluate the impact of a recently implemented policy using interrupted time series because you have no comparison group and the policy was implemented all at once on the ...
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How does the sliding window work?

I am not sure how the "Sliding window" method work. Let's assume I have a dataset of number of logins by hour. a) A window of 24hours to predict the next 24h? b) A window of 24h to predict the next ...
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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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147 views

How to compute the change of Ridge regression solution when one row of data changes?

I understand that $\boldsymbol{\beta} = (X^TX + \lambda I)^{-1}X^T\mathbf{ Y}$ is the closed form solution of Ridge regression. So sometimes, when I run a rolling window, meaning everytime I run the ...
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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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2k views

What is rolling mean and standard deviation in terms of stationarity?

I would like to know what a rolling mean and rolling S.D means in terms of achieving stationairty concerning a time series? I ran an ADF test and it told me my time series was stationary however, by ...
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Classify time series with unequal lenghts [closed]

I have a set of time series sensor measurements (acceleration and gyroscope readings) for driving events (harsh acceleration, harsh brake …) with the type, start and end of each event. I need to ...
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44 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 ...
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277 views

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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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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108 views

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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209 views

Impact of window size on estimated volatility using SMA or EWMA

When calculating volatility (either using an SMA or EWMA approach), what impact does the window size have on the volatility estimate?
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147 views

Markov Switching GARCH - Expanding or Rolling window forecasting?

When modelling volatility do people tend to use expanding or sliding windows to predict the performance of MS GARCH models?
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103 views

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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268 views

Moving window time series

I am working on some computations for large datasets of market data and I was wondering is there is a simple way to apply the following logic without using heavy looping. I will simplify the problem: ...
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37 views

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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653 views

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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933 views

Detrending using moving average

In Terrence C. Mills book Im currently reading The Foundation of Modern Time Series Analysis on while discussing Person's detrending methods, he mentions the following result. ... Concider the ...
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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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325 views

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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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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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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292 views

Time Series: relating duration of one time series to events

I have a stats question, and not enough experience to even begin to know how to find the answer. It's a time series question relating two time series: A = anomolous conditions (-,0,+) --- continuous ...
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1k views

Difference between 'Time domain features' and 'frequency domain features'?

I have a time series data of accelerometer in X,Y,Z axis. Data is not sampled at a constant sampling rate(but is close to 100 Hz). In the paper I am referring, it mentions that for feature selection I ...
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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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467 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 ...