Search Results
| Search type | Search syntax |
|---|---|
| Tags | [tag] |
| Exact | "words here" |
| Author |
user:1234 user:me (yours) |
| Score |
score:3 (3+) score:0 (none) |
| Answers |
answers:3 (3+) answers:0 (none) isaccepted:yes hasaccepted:no inquestion:1234 |
| Views | views:250 |
| Code | code:"if (foo != bar)" |
| Sections |
title:apples body:"apples oranges" |
| URL | url:"*.example.com" |
| Saves | in:saves |
| Status |
closed:yes duplicate:no migrated:no wiki:no |
| Types |
is:question is:answer |
| Exclude |
-[tag] -apples |
| For more details on advanced search visit our help page | |
Results tagged with missing-data
Search options not deleted
user 3382
When the data present lack of information (gaps), i.e., are not complete. Hence, it is important to consider this feature when performing an analysis or test.
1
vote
Estimate missing data in time series
Take the data from 2011 to 2013 and reorder it so that the first value is the true value for 12/2013 , the second value is 11/2013 and the 36th value is 1/2011 . Now forecast/model this series for 48 …
- 30k
0
votes
Missing daily data for one month
What I would do is to use day-of-the=wekk averages for the 4 months surrounding the "missing month" to obtain initial estimates of the daily vales for the "missing month". I would then entertain a mod …
- 30k
1
vote
Is there an R package with a pretty function that can deal effectively with outliers?
I encounter this quite frequently when dealing with customer daily time series data. It appears that many accounting systems IGNORE daily data that didn't occur i.e. no transactions were recorded for …
- 30k
3
votes
Accepted
Dealing with large time series gaps
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 th …
- 30k
5
votes
Accepted
Use ACF and PACF for irregular time series?
The latter approach is preferred since the time difference must be invariant/constant for an ACF/PACF to be useful for model identification purposes. Intervention Detection can be iteratively used to …
- 30k
3
votes
Accepted
How to predict missing values in time series?
This data has similar statistical characteristics identical except for the placement of the anomalies ( i.e. non time series data riddled by a number of pulses AND missing values as Which model shoul …
- 30k
-1
votes
Can i use autoencoder for predicting time series missing data?
Intervention Detection can be used to predict/replace missing values. see http://docplayer.net/12080848-Outliers-level-shifts-and-variance-changes-in-time-series.html
A downvote probably motivated by …
- 30k
1
vote
Forecast (impute) missing discrete values in multiple time series
If I had your problem and my favorite piece of software , I would develop two daily models for the two regimes of observed data. The first model would predict the missing observations using approaches …
- 30k
0
votes
Groups of correlated time series prediction
You have a VECTOR ARIMA problem (VARIMA) where you have k1 endogenous series ( the Y series ) and k2 exogenous series ( the X series ) . Identify a useful VARIMA model which book can help me for self- …
- 30k
1
vote
How to deal with gaps/NaNs in time series data when using Matlab for autocorrelation and neu...
Use Intervention Detection to impute the missing vales exploiting the useful ARIMA structure and any local time trends and/or level shifts.
- 30k
5
votes
STL on time series with missing values for anomaly detection
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.pd …
- 30k