All Questions
Tagged with moving-window time-series
49 questions
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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
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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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
3
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1
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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 ...
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
1
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1
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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
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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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2
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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
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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
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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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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
1
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350
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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 (...
- 77
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
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
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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
2
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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
3
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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
2
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0
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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
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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 ...
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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
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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
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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
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2
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972
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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 ...
- 80
3
votes
1
answer
978
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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 $2$: $x_2,\dots,x_{w+1}$,
$\dots$,
...
- 69.5k
1
vote
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
2
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1
answer
68
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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 ...
- 175
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0
answers
748
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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 ...
- 11
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1
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5k
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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 ...
1
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0
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47
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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 ...
- 11
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0
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115
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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
2
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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
9
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1
answer
15k
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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 ...
- 1,250
2
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1
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282
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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?
- 265
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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
2
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2
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1k
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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, ...
user185498
2
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0
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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
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1
answer
439
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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 ...
- 31
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1
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1k
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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 ...
- 213
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0
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531
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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
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0
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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
4
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2
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7k
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Selecting ARIMA Order using Rolling Forecast
I'm wondering if a rolling forecast technique like the ones mentioned in Rob Hyndman's blogs, and the example below, could be used to select the order for an ARIMA model?
In the examples I've looked ...
- 125
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1
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4k
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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 ...
- 125
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1
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876
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Question about rolling forecast horizon
I'm trying to understand how the rolling forecast example below from Rob Hyndman's blog works. In the final line of the for loop, is ...
- 125
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1
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2k
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Are rolling forecasts more accurate that full-sample forecasts?
I compared the auto.arima forecast checkts below to the rolling forecast fc and noticed ...
- 249
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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 ...
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6
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1
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1k
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Benchmarking time series forecasting model
Problem: I'm building a time series forecasting model for daily data wherein, the aim is to forecast for the next one week. So, to validate the model, I'm using a moving window based validation ...
- 253
3
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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
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1
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2k
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What is the autocorrelation function of a time series arising from computing a moving standard deviation?
Say I have a time series of observations and I compute a measure of the variance of that time series as the standard deviation (SD) in a rolling window of width $w$ and that window is moved in single ...
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