# Tagged Questions

Time series are data observed over time (either in continuous time or at discrete time periods).

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### Interpreting coefficients of first differences of logarithms

My problem is interpreting coefficients of such time series model: $$\ln Y_t - \ln Y_{t-1} =b_1 \cdot \left(X_{t}-X_{t-1}\right)+b_2 \cdot Z_t.$$ I don't know how to ...
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### Robust outlier detection in financial timeseries

I'm looking for some robust techniques to remove outliers and errors (whatever the cause) from financial time-series data (i.e. tickdata). Tick-by-tick financial time-series data is very messy. It ...
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### Finding the correct data mining approach

(I apologise for being a newb, but I'm a researcher introducing myself to data mining---any help or insight would be greatly appreciated. Also, this isn't technically a homework question, but I've ...
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### Interpreting a regression modeled on twice differenced data

I have built a OLS model with data that was twice differenced. As I understand (and maybe I'm wrong) the coefficients (betas) can be applied to the original undifferenced data to provide Y at that ...
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### What is the impact of windowing function on time series

Greeting I would like to know what is the impact of windowing functions like Hanning,... on a time series. Is it possible to finde anomalies using windowing functions? EDIT I have a time series, ...
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### In case of multi-time series data, does the significant linear model gives there is something more?

I have total of 9 time series data. I'm basically using 8 variables as my X and 1 variable as Y. I've tried VAR model today but I don't see ANYTHING. However, when I do linear regression, all of the ...
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### Moving-average model error terms

This is a basic question on Box-Jenkins MA models. As I understand it, an MA model is basically a linear regression of time-series values $Y$ against previous error terms $e_t,..., e_{t-n}$. That is, ...
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### Identifying volatile “moves” in a price timeseries

Necessary advance apologies: I am not a statistician, so I hope I am able to make my question as clear as possible. I have a price timeseries... say, something like this, but much more granular: ...
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### Time-series data evaluation by PCA / EOF

I have got time series data of a crop attribute, each time step with 500 data points on a spatial grid. 2 measurements were conducted in the first year, 3 in the consecutive year. I want to evaluate ...
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### Fitting a GARCH(1,1) model

How much data is needed to properly fit a GARCH(1,1) model?
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### Help with panel-data in excel

I want to know if the initiation of a state Renewable Portfolio Standard affects the level of renewable energy output in that state. I don't have access to Stata right now, so I'm stuck using excel. ...
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### Best imputation method for stochastic noisy data?

What is the best imputation method for a dataset consisting of stochastic data? For example, let's say you have a table of security returns. In some cases the missings are random, in other cases are ...
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### Confused with basic time series terminology

I am a bit confused with the basic time series terminology: Consider the following words: fitted values forecasted values in-sample forecasts out-of-sample forecasts in-sample fit I am using ...
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### Sparse variable selection algorithms that account for parameter changes

I am variable selecting for a time-series forecasting model that has parameters sampled from a high variance sampling distribution centred near zero and that undergo changes over time. Each predictor ...
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### Probability as a dependent variable in a time-series regression

Are there any issues to run a Newey-West time-series regression on a dependent variable that is a probability? What are the biases that I am facing? I can't find anything online that can help me out ...
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### Practically handling many non-stationary forecasting predictors

This question is about specific strategies to deal with non-stationary variables in forecasting. This problem usually rears its ugly head when you have a predictor whose levels are relevant to the ...
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### Outlier detection and smoothing for multi dimensional time series

From kinect depth images, I have collected the following time series that represent the features = 3D joint positions, quarternion angles, difference between hip joint and centroid of the right arm ...
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### Differences between clustering and segmentation

I have read about piecewise aggregate approximation (PAA) mining time series data, sliding window, top down and bottom up approaches for time series segmentation but these are applicable to single ...
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### Time Series Modeling with Lagged Variables

I have a dataset with columns that represent lagged values of predictors. To illustrate with a simple example, suppose we had car sales data for 3 years and the only predictors available were income ...
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### Arima residual calculation and comparison with R

I have simulated an ARIMA(1,0,1) process using R. Below is the code. ...
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### Finding anomalies using moving average in a time series [duplicate]

I want to find anomalies in a time series. Is it possible to find anomalies using moving average?
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### Frequency of time series in R

I have 10 minutes-intervaled wait-times data for a coffee shop for 4 weeks between 9am-5pm. I use R's ts for my analysis. What should be the frequency parameter? Is it (48=# of intervals per day) or ...
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### Forecasting binary time series

I have a binary time series with 1 when the car is not moving, and 0 when the car is moving. I want to make a forecast for a time horizon up to 36 hours ahead and for each hour. My first approach ...
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### Estimation of Missing Observations

I have a data monthly as well as daily data of no of patients and I want to employ time series model .How can I estimate missing counts.Any one would please guide me
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### Moving average filter for outlier removal

I am using a moving average filter to smooth data for outlier removal. By changing the number of average points, I am getting different result. My data are multi-dimensional feature vectors. I ...
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### What part of an ARMA model requires a stationary time series - the AR or the MA?

Could I use a non-stationary time series with simply an Autoregressive model?
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### Test when and how often 2 time series have similar values

I am monitoring two moving cars and collecting data on their instantaneous speeds three times per second for approximately two hours. I would like to create some kind of hypothesis that will tell me ...
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### VEC model with lags restrictions

I am trying to estimate a VEC model imposing zero restrictions manually as in the restrict() function for a VAR model. I do not know how to introduce different lags (for example lags 1 to 3 and lag 7) ...
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### What does that mean that two time series are colinear?

I am familiar with the concept of cointegration. But I hear sometimes people talking about colinearity (or collinearity) for time series. A set of points is collinear if they are on the same line. ...
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### Finding similarities using Wavelet transform

I have a time serie and I want to find similarities in it. For the first step I have calculated Haar-wavelet coefficients for this time serie, and now I don't know exactly how should I continue ...
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### How to classify data having sub-instance features?

I am trying to use machine learning on some peculiar (at least for me) data. Usually, when I do machine learning I am use to have the data in this format: ...
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### Comparing data files

We have several weather files for a year's data sampled hourly. In each we have several variables (up to ten), temperature, wind speed, solar intensity etc. I would like to try and develop a system ...
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### Non Stationary series, VAR Model

Hi I am working with a multivariate time series data consisting of 1) Demand Data 2)Sales Data 3) Rainfall Data , all available from 2010-2013,at monthly level. Approach: I am trying to estimate the ...
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### Forecasting in ARCH(1) models

I read this definition of an ARCH(1) model: $$r_{ŧ}=\sigma_{t|t-1}\epsilon_{t}$$ $$\sigma^{2}_{t|t-1} = \omega + \alpha r_{t-1}^{2}$$ However, when it comes to forecasting the h-step-ahead variance, ...
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### Handling stationarity issues in proc ucm/state space time series models

Hope I'm able to find someone who can answer this question. The previous one didn't get answered! Proc ucm is the SAS implementation (using state space concepts) to isolate the unobserved trend, ...