IrishStat
  • Member for 10 years, 11 months
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  • Warminster, PA, United States
What test is performed to see the effect of one variable on another variable in time series analysis?
0 votes

Please see "user:3382 transfer function" for some of my reflections/advice on how to test the impact of a time series on another time series. Care should be taken to "identify/allow&...

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Outlier detection in seasonal time series via forecasting with ARIMA model
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1 votes

I took your 61 consecutive days of data (24 hourly readings per day): The 1464 values were not analyzed in one model because there were essentially 24 sets of 61 historical values piggy-backed ...

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How to correct outliers once detected for time series data forecasting?
5 votes

When you identify an ARIMA model you should be simultaneously identifying Pulses/Level Shifts/Seasonal Pulses and/or Local Time Trends. You can get some reading material on Intervention Detection ...

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When (and why) should you take the log of a distribution (of numbers)?
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119 votes

If you assume a model form that is non-linear but can be transformed to a linear model such as $\log Y = \beta_0 + \beta_1t$ then one would be justified in taking logarithms of $Y$ to meet the ...

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Why difference a time series for forecasting?
0 votes

Let the data speak to the issue of which approach is more correct for any individual time series. This is what I have early championed which was ultimately and independently supported by Makridakis ...

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How to predict the standard deviation that is changing over time?
-1 votes

when you have a useful model the residuals should have constant variance i.e. not dependent on level and not autoregressive in nature. Thus the residuals need to have homogenous error variance. If the ...

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ARIMA model has trouble forecasting next month
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2 votes

Whenever possible, it is best to develop one equation that effectively characterizes the data see “Joint estimation of all parameters is preferred.” from lecture 3 http://faculty.chicagobooth.edu/ruey....

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How to interpret the constant for an ARMA model
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3 votes

Your equation is $$[y(t)-6.8840][1-.9916B]= +ϵ(t)$$ or $$y(t)= .0084\times6.8840 + .9916\cdot y(t-1)$$ $$y(t)= .0578 + .9916\cdot y(t-1)$$ What has you confused is for your stationary model the ...

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Transform a non-stationary time series to perform ARIMA
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1 votes

Took your 2193 daily values and introduced them to AUTOBOX which detected both a significant persistent day-of-the-week pattern :day 1 & day2 (Saturday & Sunday) ... both negative and a ...

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Could adding more history (expanding the training sample) reduce forecast accuracy?
1 votes

Thanks for the question as it leads to a teaching moment .... An often overlooked caveat when dealing with data is the assumption that the parameters to be optimized are invariant . In practice with ...

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What model should one use for this short time series?
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1 votes

You can just use the history of Y or also your suggested causal. I have not seen “sample of sales” before as a causal, so I am hesitant to want to use that variable, but I am sure you know what you ...

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How do I incorporate an innovative outlier at observation 48 in my ARIMA model?
3 votes

If $$Y(t) = [\theta/\phi][A(t)+\text{IO}(t)]$$ then $$Y^\text{*}(t) = [\theta/\phi][A(t)] + [\theta/\phi][\text{IO}(t)].$$ If $$\theta = 1\ \ \text{and}\ \ \phi = [1-.5B]$$ for example ... then $$Y^\...

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Multivariate ARIMA with regression
1 votes

Your data set / design matrix tells a lot about your assumptions. You are explicitly assuming that week days have a common effect and weekends have a common effect. It is much more general to estimate ...

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Forecasting/predicting total sum of donations (following GLM with poisson family and log link)
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1 votes

ROUND TWO: You asked “how do I do this with the log-link function and quasi(Poisson) errors?”. I say put aside your priors suggesting a particular fixed model and use a data-driven empirical process ...

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auto.arima warns NaNs produced on std error
4 votes

Your problem arises from an over-specification. A simple first difference model with an AR(1) is quite sufficient. No MA structure or power transform is required. You could also simply model this as a ...

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Detect trend in time series
0 votes

Your data: If you know a priori what the form of the equation is then as others have pointed out it is trivial to estimate parameters. A more general solution is to characterize the data with an ...

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Dealing with large time series gaps
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2 votes

This problem arises quite naturally with predicting beer sales where the beer is only sold say for 5 months of the calendar year... e.g. August, September, October, November and December. Nominally ...

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Variance stabilizing transformation for time series
1 votes

If the variance of the model errors is proportional to the expected value the power transforms may be appropriate When (and why) should you take the log of a distribution (of numbers)? ... your data ...

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Why SARIMA has better accuracy on weekly dataset than on daily one?
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I have examined your 2 data sets ... DAILY and WEEKLY and found that your model specification for DAILY is WAYOVERCOMPLICATED while your automatic arima approach was inadequate/deficient. Both data ...

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getting Significant seasonality on a straight line
2 votes

after receiving the data from the dropbox , I have some interesting things to report using AUTOBOX , a time series analusis package that I have helped to develop. To some it would appear that ...

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How does taking the log of the variable solve our problem of heteroscedasticity?
2 votes

If the variance of the errors is proportional to the expected value then a logarithmic transform transform is appropriate. Other possible relationships between the first and the second moment would ...

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Write ARIMA equation l from arima with drift output inR
0 votes

Standard arima software requires that the drift parameter MUST be included if there is no differencing incorporated otherwise it is optional. I have seen and used software where drift is ALWAYS an ...

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What does it mean when the Box-Cox pre-calculated lambda is negative?
0 votes

your lambda value is nearly 0. , Why are you transforming the observed series ...all the assumptions are about the error series from a model. When (and why) should you take the log of a distribution (...

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How would a select an ARIMA model based on the ACF and PACF?
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1 votes

Sometimes too much of a good thing is not so good !. Your 1201 monthly values starting at at 1920/1 is such an example. The historical plot is hysterical suggesting that one might start with the ...

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Detecting if weekday has influence on data - is using F-test correct method?
0 votes

you don't want to simply introduce 1 series into the regression model with values [1,2,3,4,5,6,7,1,2,3.....] which assumes linearity between days ' BUT rather introduce 6 dummy 0/1 indicators ...

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Time Series Modelling problems resources
1 votes

You asked 'I am looking for some resources with example problems with Time Series modelling and predictions" I answer .. look no fUrther that SE... don't limit your scholarly pursuit to a language ...

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Analysis of time series data with peaks for counts of occurrences
1 votes

Data can have monthly effects , week within a month effects besides the effects of environmental changes. Post one of your data sets for a particular location and time available I will try to help ...

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How should you determine the order of an AR(p) model using PACF with fluctuating significance?
0 votes

This is probably due to untreated deterministic (NOT STOCHASTIC) effects like level shifts or local time trends in the original series and is not representative of a recurring autoprojective (arima) ...

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Simulation of correlated stochastic processes based on some time series
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Time series simulation based on a given time series might help explain how data can be generated for a user-specified model. In this regard it is possible to specify a model without having generated ...

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Selecting correct frequency for time series data
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Having experiebce with hourly temperature data , I can suggest that your data most likely has two-seasons ...24 and 7 and probable monthly effects to deal with lunar effects AND of course possible ...

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