Tagged Questions
4
votes
1answer
24 views
How to model month to month effects in daily time series data?
I have two time series of daily data. One is sign-ups and the other terminations of subscriptions. I'd like to predict the ...
0
votes
1answer
14 views
ARIMAX and xreg variables
Just wondering how to setup xreg variables for ARIMAX models? I am particularly interested in whether I should be grouping together events for dummy variables. For example should I create 1 dummy ...
2
votes
0answers
22 views
Algorithm to detect if a particular event (date) has an impact on a time series
I have many time series and a set of dates (key events eg public holidays or big sales days). There are too many for me to look as manually and determine if either an increase or decrease occured as a ...
4
votes
2answers
95 views
The way an MA(q) model works
I am trying to understand the way MA(q) models work.
For this purpose I have created a simple data set with only
three values. I then adapted a MA(1) model to it. The results
are shown below:
...
0
votes
1answer
35 views
Using the rugarch package when sample size is small
I have a question about the rugarch package.
My sample size is 43 and I have a problem to model a garch whose mean equation includes an exogenous model; otherwise ...
1
vote
0answers
62 views
Repeated Measures in R, SAS or SPSS: Whale Body Temperature Data
I have a question regarding a repeated measures experiment. I have 5 whales (randomly selected) that I recorded body temperature on. Data was collected via satellite, programmed to transmit records at ...
0
votes
0answers
53 views
AIC vs BIC vs MDL
I am trying to learn the difference between the three approaches and their applications.
a) As I understand,
AIC = -LL+K
BIC = -LL+(K*logN)/2
Unless I am ...
2
votes
1answer
46 views
Interpolation of influenza data that conserves weekly mean
For each week, I have the following count data (one value per week):
Number of doctors' consultations
Number of cases of influenza
My goal is to obtain daily data by interpolation (I thought of ...
2
votes
0answers
46 views
Best practices for dealing with shifting, inconsistent seasonality
This question is related to a previous post I've looked at (Calculation of seasonality indexes for complex seasonality), but deals with more granular data (daily instead of weekly), and transforming ...
0
votes
0answers
36 views
Reconstructing time series data with missing values from external data source
I have a 100 year time series. But the first 30 years are missing. So I correlated the 70 years that I have with another (100 year) time series to predict back / reconstruct the missing values. For ...
2
votes
0answers
38 views
Dummy variables for time series
I'm a new user on R. I'm stuck on my times series research currently with the some questions. Not sure anyone can help me.
Dummy variable.
I wanted to add more than 1 dummy variable in the model. ...
1
vote
1answer
72 views
First steps learning to predict financial timeseries using machine learning
I am trying to get a grasp on how to use machine learning to predict financial timeseries 1 or more steps into the future.
I have a financial timeseries with some descriptive data and I would like to ...
1
vote
2answers
122 views
Forecast total for a year given monthly time series
I have a monthly time series (for 2009-2012 non-stationary, with seasonality). I can use ARIMA (or ETS) to obtain point and interval forecasts for each month of 2013, but I am interested in ...
2
votes
1answer
90 views
How to forecast hourly data in R
I have hourly login data for a web site. Certain hours of the day for example between 09:00 and 12:00, there are heavy traffic on the site. I would like to forecast the hourly data for about one year.
...
4
votes
2answers
143 views
Data visualization of average and standard deviation over a small time series
I am trying to find the best way to visualize the following data:
I have values for 3 different times/dates, each time/date has the same 20 species. For each species I have the average height and ...
0
votes
2answers
55 views
How to forecast time series with the help of other training time series in R?
Consider such a task: I have the figures on last year's bookselling of a certain book store, and the information of the first half of this year too. Now I want to predict the figure at the end of this ...
0
votes
2answers
47 views
Time Series Similarity : Differing Lengths with R
I am experimenting with creating a distance matrix between time series for clustering and similarity searching. The main reference I am using is for the Similarity procedure in SAS (Paper). I would ...
4
votes
1answer
119 views
Time Series Forecasting with Daily Data: ARIMA with regressor
I'm using a daily time series of sales data that contains about 2 years of daily data points. Based on some of the online-tutorials / examples I tried to identify the seasonality in the data. It seems ...
1
vote
1answer
24 views
How to accurately track the 75% quantile in a non-stationary timeseries?
I have a non-stationary timeseries with a mean (ยต) and standard deviation (SD) which both vary across time. The distribution of the timeseries is skewed, so the left and right sides of the ...
0
votes
0answers
16 views
raster images stacked recursively [migrated]
I have the following problem, please.
I need to read recursively raster images, stack and store them in a file with different names (e.g. name1.tiff, name2.tiff, ...)
I tried the following:
...
3
votes
1answer
79 views
Approaches to Forecasting with Daily Timeseries
I have just started to learn about forecasting. I thought it would be easy to create forecast models for a daily time series but have encountered a number of difficulties. Firstly most examples and ...
0
votes
0answers
8 views
Generating yearly frequency plot from multi-year data [migrated]
I have hourly wind speed data in the following format
...
1
vote
2answers
47 views
With regard to ARMA time series, what exactly is eacf (extended auto-correlation function)?
I'm going through my copy of Analysis of Financial Time Series, 2nd Edition, and I'm at the ARMA portion. One of the techniques for model selection is computation of the extended auto-correlation ...
1
vote
2answers
100 views
In R, coefficients of MA function are wrong?
I'm currently sifting through my copy of Analysis of Financial Time Series 2nd Edition by Ruey Tsay, and one of the sections involves fitting a MA model to certain data (data set is here). Here's the ...
1
vote
2answers
77 views
Predictions of a monthly temperature time series: adding noise to the predicted values
I am doing predictions on monthly temperature data for 100 years, from 1901 to 2000 (i.e 1200 data points). I want to know if the method I follow is correct because in my output, I do not see the ...
0
votes
0answers
34 views
R Packages for Panel GARCH?
Are there any packages that let me estimate panel GARCH models in R?
I have looked extensively in Google but have not found anything.
3
votes
2answers
74 views
Confidence bands in case of fitting ARIMA in R?
I want to look at the acf and pacf of my data, to identify the model for my mean equation, so I want to fit an ARMA for my mean equation and later on model the conditional variance by a ARCH/GARCH (I ...
2
votes
0answers
63 views
Confusion with Augmented Dickey Fuller test
I am working on the data set electricity available in R package tsa. My aim is to find out if an ...
0
votes
3answers
106 views
Identify seasonality in time series data [duplicate]
I want to detect presence of seasonality in time series data. I know one can achieve that by plotting the autocorrelation function but I need an automatic process if the series is seasonal or not, ...
3
votes
2answers
233 views
Correlogram in R like in Stata?
In STATA I can create a "Correlogram" to find the appropriate lag order in case of time series. E.g.
I know I can use the acf or Acf of the forecast package to calculate the ACF and PACF and to ...
2
votes
2answers
54 views
Omit 0 lag order in ACF plot
How can I omit the zero lag order in an acf plot? See this picture:
generated by
...
0
votes
0answers
27 views
How to specify newxreg in prediction model of ARIMA? [migrated]
I have fit the model below to my time series data. The xreg consists of a time vector that goes from 1 through 1000 and of 12 indicator variables (1 or 0) that ...
0
votes
0answers
40 views
Seasonality Period in ARIMA function in R - How to Interpret
I've used the ARIMA function in R to fit my data to the best possible model. My data consists of daily information and there ...
1
vote
0answers
32 views
Converting ARMA models to infinite AR process in R
I'm trying to recreate some test statistics that use the infinite AR representation of normal ARMA models. I found out about the function ARMAtoMA but have not been able to find the same functionality ...
0
votes
0answers
17 views
R plot time series data: Extremely Short time laps [migrated]
I have the following data frame. The gaps between time slots are different, sometimes small, sometimes large:
...
2
votes
2answers
179 views
How to calculate multi-step prediction intervals for time series data?
I need to manually calculate multi-step prediction intervals for time series data. I know packages like 'forecast' in R provide these, but I cannot use these packages as the production infrastructure ...
1
vote
1answer
42 views
Program Impulse Response Functions for VAR
I'm trying to program impulse response functions for a VAR model using Cholesky decomposition. The thing is I do not completely understand how I should do this when I read in the literature. Suppose I ...
1
vote
1answer
39 views
How to estimate certain parameters of an AR model in R?
I need to estimate parameters of an AR model which is in the form of AR(1,11) it means that coefficients of AR orders from order 2 until order 10 are zero. How can I estimate these two parameters in ...
1
vote
1answer
61 views
What to do about Seasonality Patterns in ACF, Time Series Data
I'm dealing with a time series data and I'm trying to construct a time series model for this particular dataset. I'm new to R and tried using the the auto.arima ...
5
votes
2answers
111 views
spatial autocorrelation for time series data
I have a 20-yr dataset of an annual count of species abundance for a set of polygons (~200 irregularly shaped, continuous polygons). I have been using regression analysis to infer trends (change in ...
0
votes
0answers
46 views
“system is exactly singular” in R function BoxCox.ar
I'm trying to perform a Box-Cox transformation on some financial data (SPY).
The BoxCox.ar function (in the package TSA) gives me the following error:
...
1
vote
1answer
66 views
Kernel PCA (in R)
I am attempting to use the kernel PCA features in kernlab but am having trouble understanding the output. In particular, it's unclear what scale the results are in ...
1
vote
1answer
90 views
Positive & negative shocks in a VAR model and impulse response function in R
I have two general questions about impulse response functions in R using the package vars. Take a look at this code:
...
-1
votes
0answers
82 views
Math behind stl() function in R programming
I recently came across the stl() function in R and used it on some of my data and I'm interested in learning more about this function.
I tried reading this article, but I understood only a small ...
0
votes
1answer
69 views
how to test heteroskedasticity of a time series in R?
How can I test heteroskedasticity of a time series in R? I have heard of two tests McLeod.Li.test and bptest (Breusch-Pagan ...
-1
votes
4answers
107 views
How do I get better forecasts for this data
How do I get better forecasts for my model? Is the plot supposed to look like this? I am using the code:
fit <- auto.arima(blah)
fcast <- forecast(fit,100)
...
-1
votes
1answer
72 views
How to get p-values for model
I am using the example from Shumway's book. How do I get the p-values for all the coefficients? Do I divide the coeffients by the standard errors?
...
3
votes
0answers
34 views
How to form a confidence band around the trend fitted from time series data
I have a time series data set. I can decompose it and get the trend but I would like to put confidence ranges around the trend (past) not the forecast-ed component. The decompose function also ...
0
votes
1answer
57 views
Time series forecasting
I create time series model via:
model = sarima(data,1,0,1)
sarima.for(data,100,1,0,1)
The model diagnostics look good and indicate that the model is a good fit. ...
2
votes
1answer
99 views
ARIMA forecast with seasonality and trend, strange result
as I am stepping into forecasting with ARIMA models, I am trying to understand how I can improve a forecast based on ARIMA fit with seasonality and drift.
My data is the following time series ( over ...
