IrishStat
  • Member for 10 years, 11 months
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  • Warminster, PA, United States
auto.arima and prediction
5 votes

The reason is that ARIMA is auto-projective which uses the most recent data to compute essentially a weighted average of past values. When forecasting, the 1 step ahead is used to predict the second ...

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What method can be used to detect seasonality in data?
5 votes

Seasonality can and does often change over time thus summary measures can be quite inadequate to detect structure. One needs to test for transience in ARIMA coefficients and often changes in the “...

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Forecasting nonstationary time series
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5 votes

I have never seen a model like Box-Jenkins identification process led me to ARIMA(0,1,3) model BUT i had never seen a black swan until I went to Australia. Please post your data as it may suggest the ...

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Seasonally adjusted month-to-month growth with underlying weekly seasonality
5 votes

I model thus kind of data all the time. You need to incorporate day-of-the-week holiday effects ( lead , contemporaneous and lag effects ) special days-of-the-month perhaps Friday before a holiday ...

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How to model and make predictions on time series data in R?
5 votes

Analyzing ratios is never a good idea unless that is all you have. It is preferential to model a Y as a function of X and upon arriving at a useful model use a predicted X to obtain a predicted Y and ...

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How to model timeseries with unequally-spaced seasonality interval
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5 votes

What you need to do is to incorporate day-of-the-week , changes in day-of-the-week effects , level shift effects , local time trends , specific days-of-the-month efffects, weekly effects, monthly ...

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When was the autocorrelation function invented? And what was the motivation for it?
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5 votes

The earliest reference for autocorrelation that I can find relates to Udney Yule, a British Statistician who among other notable accomplishments developed the Yule-Walker procedure to approximate the ...

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How to interpret and do forecasting using tsoutliers package and auto.arima
5 votes

These comments are too long ...thus an "ANSWER" You are wrong it does not adjust and then identify ARIMA (as AUTOBOX does).It presumtively assumes no intervention adjustment and then rushes to ...

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Is it ok to drop the constant term?
5 votes

If including a constant term gives you coefficients that are not significant it means this variables are neutral with respect to their conditional effect and thus can be numerically either positive or ...

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How can I recognize when I must apply "log transformation"?
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5 votes

Please review When (and why) should you take the log of a distribution (of numbers)? . I have programmed this in AUTOBOX ( a commercially available time series software package which I have helped ...

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Multiple ARIMA models fit data well. How to determine order? Correct approach?
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5 votes

1)Can you still describe the ACF of the time series as cutting of despite the spikes around lag 26? 26 and 27 suggest to me that the data is weekly some sort of annual cycle pf order 26 or 52 Are ...

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Can a Moving Average be used as a dependent variable in a regression model?
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5 votes

The moving-average will be auto-correlated (even if the original series is not auto-correlated) thus this is a potential violation of the subsequent causal model. I would simply include the variable ...

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what if the residual series from arima() not looks normal?
5 votes

You might need to identify Intervention Variables like Pulses, Seasonal Pulses, Level Shifts or Local Time trends. If these are untreated they inflate the variance of the errors and you get a ...

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Data mining techniques in R for advertising and sales data
5 votes

Keeping models as simple as possible(but not too simple) is very important. There is absolutely no proof that one should incorporate seasonal differencing into a reasonable model for your data. Some ...

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What are disadvantages of using the lasso for variable selection for regression?
5 votes

I am not a LASSO expert but I am an expert in time series. If you have time series data or spatial data then I would studiously avoid a solution that was predicated on independent observations. ...

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STL on time series with missing values for anomaly detection
5 votes

ARIMA models easily incorporate dummy variables to deal with missing values. These are called Pulse Indicators . The methodology is straightforward and documented in http://www.unc.edu/~jbhill/tsay....

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How to find similarities between time series?
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5 votes

What you have is K (5) Groups where you have a dependent (water temp) and an independent series(air temp). This problems is called Pooled Cross-Sectional Time Series Analysis. Construct a separate ...

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How would you fit ARIMA model with lots of autocorrelations?
5 votes

We are working with data like this for a major fast food franchise. The series represents the demand for tacos in 15 minute intervals for the last 5 years (180,000 observations) . This series can be ...

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What common forecasting models can be seen as special cases of ARIMA models?
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5 votes

:Bruder the Box-Jenknins approach incorporates all well-known forecasting models except multiplicative models like the Holt-Winston Multiplicative Seasonal Model where the expected value is based upon ...

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Can I trust a regression if variables are autocorrelated?
5 votes

The t-statistics are un-reliable in the presence of autocorrelation of the errors. Auto-correlation in the errors can be due either insufficient lag structures in the causal variables or insufficient ...

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The "sum" of prediction intervals
5 votes

In order to compute the variance of a sum of forecasts you need to incorporate the covariance between these forecasts. Thus compute the varriance and covariance of the observed series for as many lags ...

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Testing for stability in a time-series
5 votes

As I read your question "and the fluctuations around the stable point are much smaller that the fluctuations during the transient period " what I get out of it is a request to detect when and if the ...

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What's the best formula to fit the distribution of website user number over a day
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5 votes

You have time series data and one develops an equation for intra-day usage which may use either an auto-projective ARIMA model or a set of fixed dummies (23 in number) to predict hourly expectations. ...

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How to calculate the p-value of parameters for ARIMA model in R?
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5 votes

The "t value" is the ratio of the coefficient to the standard error. The degrees of freedom (ndf) would be the number of observations minus the max order of difference in the model minus the number of ...

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Cross-validating time-series analysis
5 votes

If you have time series data then you might have a "degrees of freedom problem" . For example if you have 4 observations taken at hourly intervals and then decide to use 241 observations at 1minute ...

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What is the term for a time series regression having more than one predictor?
5 votes

This is called a Transfer Function Model. It has also been referred to as a Dynamic Regression Model.

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Can data cleaning worsen the results of statistical analysis?
5 votes

The role of "data cleansing" is to identify when "our laws (model) do not work". Adjusting for Outliers or abnormal data points serve to allow us to get "robust estimates" of the parameters in the ...

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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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How to remove non-stationarity?
4 votes

First of all … you should ALWAYS model TIME SERIES i.e. bucketed data which is observed NOT what is accumulated UNLSS you wish to first bucket/accumulate transactional data to create a bucketed time ...

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Is moving average(sliding window) a smoothing technique or forecasting technique?
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4 votes

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/...

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