Time series are data observed over time (either in continuous time or at discrete time periods).
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6 views
Python module for change point analysis
I'm looking for a Python module that performs a change-point analysis on a time-series. There are a number of different algorithms and I'd like to explore the efficacy of some of them without having ...
1
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1answer
15 views
STATA - Help for switchr command
I'm trying to perform a markov switching regression model in stata using the command switchr.
The command syntax is the following one:
switchr eq1 eq2 [weight] [if exp] [, cluster(string) ...
2
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0answers
10 views
Standardized dependent variable within a group in panel data models?
Does standardizing of a dependent variable within the identifying group make sense?
The following working paper (Deforestation slowdown in the Legal Amazon; Prices or Policies?, pdf) uses a ...
-1
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1answer
23 views
Fitting GARCH Model
I'am getting more and more familiar with this kind of model (and others models too).
I'm now used to fit this model with my data (rmgarch package in R).
How is it done ? What is the theory behind ...
2
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2answers
58 views
How do you explain 'co-integration' to determine spurious regression to a fairly new time series student?
I would appreciate it if someone could explain the concept of spurious regression intuitively / without it being too technical.
So far the student understands that spurious regression is basically ...
1
vote
1answer
40 views
How to fit a model with lagged variables
I am trying to fit a model with lagged variables.
The problem is: In a big classroom with windows open, the outside temperature, humidity and solar activity will lead to variation in the ...
4
votes
2answers
90 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
34 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 ...
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0answers
19 views
Which variable vary previously to the other one
Here are two variable (a and b) and their value through time (time.a, time.b)
...
1
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1answer
45 views
Simple mean reversion measure for binary time series
I am trying to define a simple measure for mean reversion in a stochastic sequence of ones and zeros, which I denote by $x_t$.
Yes, a unit root test on the cumulative sum could be a viable choice, ...
1
vote
2answers
91 views
Overfitting when using corrected AIC for model selection
I am using the corrected AIC to select the lag order in a simple AR(p) model. I chose the the AICc since my sample is fairly small (n=135). The AICc minimal model is the AR(15). To me it seems like an ...
1
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0answers
60 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 ...
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0answers
52 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 ...
0
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0answers
20 views
Curve smoothness - local adjacency
I am looking for statistical measures of curve smoothness.
Time-series values {(1, 0.5), (4, -0.6), (200, 1.0)} where (time-unit, value) is linearly interpolated from one to the next.
The smoothest ...
2
votes
1answer
48 views
First difference or log first difference?
I am evaluating the effect of covariances between series on returns. That is I run the following regression: $$ r_t = \beta_0 + \beta_1\text{Cov}(Y_t,r_t) + ...$$
I have conducted my analysis with ...
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0answers
32 views
Block bootstrap for dependent data with unequal sampling intervals?
I have data from a natural archive (lake sediment). For various reasons it is usually impossible to sample the archive equally in time, and we end up with a time series where essentially we have ...
2
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0answers
44 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
2answers
66 views
State space models for time series forecasting
I am new to time series forecasting and have been slowly working my way through the different approaches available. I've so far mainly been using ets and arima models available in the R forecast ...
0
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0answers
35 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 ...
0
votes
1answer
35 views
What is the proper name for a backward forecast?
Suppose in time series you have the data in a recent period and you would like to use that data to extrapolate backward to get estimates of the time series back in time. What do you call that? ? ...
1
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1answer
45 views
when to aggregate when time series forecasting
I have a some historical sales data for various product SKUs, including category information ("department" "category", "subcategory"). I want to use this to generate sales curve (a baseline forecast ...
2
votes
1answer
31 views
ACF and PACF of AR process with non-zero mean
Calculating the acf and pacf of an AR process with zero mean is straightforward, but does anyone knows how to proceed when the mean is not zero? Of course my intention is to calculate the theoretical ...
2
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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. ...
0
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0answers
22 views
Chow's test and serially correlated model errors
how can one handle a time series with the Chow's test (in order to find a structural break) so that the assumption of independent model errors holds? I'm using the R function chow.test {gap}
6
votes
1answer
129 views
Can you compare AIC values as long as the models are based on the same dataset?
I am doing some forecasting in R using Rob Hyndman's forecast package. The paper belonging to the package can be found here.
In the paper, after explaining the automatic forecasting algorithms, the ...
0
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1answer
68 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
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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
86 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.
...
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0answers
13 views
Is spectral leakage a random variable?
Spectral leakage is obviously a function of several factors, including its underlying algorithm, precision of variables, etc.
Is there a body of work that explores the behavior and attempts to ...
0
votes
1answer
93 views
Does this histogram of residuals indicate that the residuals are effectively random?
I am studying a univariate and discrete time series. I know that residuals should be effectively random and have a good fit, and should have a bell shape.
Does the plot below suggest that the ...
0
votes
0answers
12 views
SK test for time series comparison or alternative?
I am evaluating the ability of the data derived from keywords typed in a search engine to detect outbreaks of a certain disease, by comparing it to the laboratory data for the same data. So I have two ...
0
votes
0answers
16 views
How should I interpret the (Bartlett) lag window in this equation?
I'm trying to apply a technique for identification of structural breaks in a financial time-series (so heteroskedastic) and one of the estimators is defined as follows:
$$
\hat{\omega}_4 = ...
2
votes
1answer
43 views
What do error bounds in forecasting represent?
What do error bounds actually mean in forecasting timeseries? For example, when I get a forecast I get the 85% and 95% high and low error bounds. I can also set my own error bounds to be calculated ...
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0answers
30 views
Implementing an ETS and ARIMA forecast
I've been using the R Forecast package which I have used to create fitted ETS and Arima models. I can easily predict ahead within R using the Forecast package but need to be able to do prediction ...
4
votes
2answers
135 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
0answers
13 views
Step detection segment
Is it possible to identify a segment (not point) of structure change within a time series? Ideally, I want something similar to a way to look at a time series and use the breakpoint function within ...
2
votes
0answers
39 views
Time Series Analysis (ARIMA) with Logistic Regression and Control variables
I'm planning to do a study on readmissions in a certain hospital unit and I want to study the impact of an intervention on readmission rate (binary variable), while controlling for individual-level ...
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
46 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 ...
1
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2answers
53 views
Removing seasonal and non-seasonal oscillations with least-square method
I have data on sea temperatures at different depths. With these data I need to remove seasonal and non-seasonal oscillations by fitting a function that consists of two sinusoids with periods of 12 and ...
4
votes
1answer
116 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 ...
1
vote
0answers
21 views
Interpretation of error term in Moving Average (ARIMA)
I have an elementary question regarding the error term in MA (ARIMA)--
From where does this error term come from?
From what I understood from the question raised earlier in the following link: ...
0
votes
0answers
12 views
Adjusting mixes of categorical variables by duplicating records
I have a large table with 4 columns, MONTH, COLOR, REGION and RESULT with the results of various contests that are repeated many times each month for the different colors. I can get the result to see ...
0
votes
1answer
44 views
Is there a statistical model for modelling variables that are measured in varying amounts and in different time points per individual?
I have been trying to model a dataset of variables where each individual is measured a different number of times, and on different point in time. Most of my variables are count, but some are not (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
74 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
...
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0answers
23 views
Harmonic or dummy seasonal model
Within the BFAST package in R, one of the parameters that it gives is the choice of seasonal model parameter (harmonic, dummy, or none). I understand what none does; However, I didn't really ...
