Tagged Questions

1
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1answer
34 views

How to correctly model noise?

Assume a linear mixing model $x = As$, where $x = (x_{0}, ..., x_{n})^T$ are linear mixtures of $s = (s_{0}, ..., s_{n})^T$, and $A$ is the mixing matrix. Now, if I introduce additive noise to this ...
0
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3answers
108 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, ...
0
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1answer
65 views

Modelling two correlated variables

I wish to simultaneously predict two correlated time series. Here is a plot of one time series against the other: At the moment I have separate linear regressions for both of them which rely on ...
1
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2answers
106 views

What model can I use to describe the following time-series?

I'm wondering if someone might be able to help me locate an appropriate model for the following two time-series (the cyan and blue one, the reds are rolling means). I'm looking more for a general ...
0
votes
1answer
61 views

Trying to fit a model after detrending

I have data for Hydrogen Sulfide Series, see here http://www.wikiupload.com/Y4WAZJ4Z0IMTK7V I applied a Box-Cox Transformation with $\lambda =1/3$ to try to stabilize the data. I plotted a few sample ...
1
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0answers
24 views

Pattern of events that leads to a goal

I am not a statistician, so go easy on me here... I have collected lots of data on the behaviour of customers in a shop. A customer will enter the shop and a series of events will then take place ...
0
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0answers
39 views

Issue in system identification using black box modeling and Yule Walkers Equation

I have a set of observation, a vector consisting of 500 positive real values. I am having a tough time in figuring out what should be the model, i.e a general equation representing the data values. I ...
0
votes
2answers
187 views

Time series prediction - what is Autoregressive Tree model ? (Python)

Our problem: model evolution of values of a continuous variable over time. I came through a paper presenting an approach for predicting the next values for a time series. Whereas ARIMA model is more ...
1
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1answer
599 views

Seasonal differencing in Arima function in forecast package in R

I just want to ask about the Arima function in forecast package. The usage of it is, ...
0
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0answers
59 views

Present Value Model: how to determine the price at the beginning of period $t$

I want to estimate coefficients of the Present Value Model of a stock exchange (as defined here). The model uses just two variables, namely: "price at the beginning of period $t$" and "forthcoming ...
0
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0answers
96 views

Analyzing online sales where data are only produced when the sales is made

I am trying to model data on the number of online sales are made within a fixed sale period of 3 days. Data are generated only when the sale is made. I think for this kind of data I will be using a ...
4
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3answers
442 views

What model can be used when the constant variance assumption is violated?

Since we can not fit ARIMA model when the constant variance assumption is violated, what model can be used to fit univariate time series?
1
vote
2answers
168 views

How to model market share or a fraction of anything that sums to 100%?

Suppose we have sales of 3 products A, B and C which market share always sum up to 100%. How to model market share of product A using market shares of B and C? So that we will know that if share of A ...
3
votes
1answer
127 views

How to fit a time series model for a large dataset?

I need to compare the accuracy of time series modeling and neural network techniques. As we all know, large data set is needed for neural networks. Since I'm comparing both techniques, I have to ...
3
votes
0answers
81 views

Time series modeling the number of users of a mobile app

I want to model the number of users of an mobile app. This app has two kinds of users: free and paid. I thought of this autoregressive model: $x_t = Ax_{t-1}$ with $x_t$ being a 4-dimensional ...
1
vote
1answer
68 views

Is there a non-parametric test for whether a series' unconditional variance has changed over time?

I'm not sure how this would look. It seems you've have to specify regime breakpoints within the data, right? Or is there some rolling method that could compare window sizes against each other? Also ...
4
votes
3answers
210 views

What model should I use for this research?

I am currently working on a project dealing with time series data but I have little experience with time series analysis so I was hoping to get some direction on what kind of exploratory data analysis ...
3
votes
2answers
323 views

Understanding forecasting in R

I am presently trying to learn R. I would like to be able to apply it more in my work environment as I am an analyst in the Health Care industry. I am presently trying to use R to forecast. What is ...
1
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0answers
40 views

Modeling a TAR model that can handle the problem of outliers in nonlinear data

Can somebody please share idea on how I can model a TAR model that can handle outliers in nonlinear data? I need to compare such model with the general form of TAR model. Which computer software can ...
4
votes
1answer
169 views

How to use statistics to help buy a house?

Despite the title, this is a more general and clearly solvable problem, I just can't research it because I have no idea what it's called. Assume you are trying to buy something (such as a house) ...
4
votes
1answer
96 views

How to handle changing definitions of regions over time in data?

I just found out that my dataset is a lot messier than I expected and I was wondering if anyone here had some advice. I have sales data that is divided into regions (5 big breaks on a national level ...
0
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4answers
157 views

What fields use modelling for analysis of a single time-series?

What fields of science (social or physical) use modelling for time-series analysis? In particular, time-series that cannot be replicated? Two examples that I can think of are climate modelling, and ...
1
vote
1answer
121 views

Algorithms for predicting a couple points in the future

I'm familiar with supervised learning algorithms like regression and neural networks which look at a bunch of input points and learn a function which outputs a value (the value varying depending on ...
8
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3answers
3k views

How to fit an ARIMAX-model with R?

I have four different time series of hourly measurements: The heat consumption inside a house The temperature outside the house The solar radiation The wind speed I want to be able to predict the ...
1
vote
1answer
65 views

Disagregating overall growth into contributing factors

I have a dataset with library document holdings per publicationyear, which I can query for the frequency of terms, subjects, etc. That allows me to construct times-series of the "popularity" of ...
4
votes
3answers
346 views

Forecasting time series based on a behavior of other one

Apologies for this vague and unclear question, I have no background in statistics. I have two vectors of time series data, covering a six month period. The data is in daily intervals (except for ...
1
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0answers
321 views

How to model categorical (discrete-valued) time series?

Just want to make a little survey, What are, according to you, the best approach to model categorical time series? I'm building a model able to generate time series reproduicing the characteristics ...
2
votes
2answers
587 views

Using k-fold cross-validation for time-series model selection

Question: I want to be sure of something, is the use of k-fold cross-validation with time-series is straightforward, or one need special attention before using it? Background: I'm modeling a time ...
4
votes
0answers
259 views

How to model time-varying correlation

Suppose I have two time-series variables, $\{x_t\}$ and $\{y_t\}$, where $t\in[1,T]$. I would like to model the correlation $\rho(x_t,y_s)$ as some function of $t$,$s$, and the difference $t-s$. In ...
2
votes
1answer
136 views

Model the proportion of a subset of total counts to determine the difference

I've been working on solutions to my first unanswered questions and had been proposed to rather model the proportion of total count of deaths that are unnatural death counts. The reason why I want to ...
3
votes
2answers
237 views

Constant term in time series econometric models built on 1-st differences

Both dependent and independent variables I deal with are nonstationary series that become stationary after differentiating them once. The problem is that I assume that the dependent variable has a ...
2
votes
1answer
397 views

Time-series data pre-aggregated into non-stationary rolling 12-month periods: are there special considerations for modeling?

I'm exploring the use of changepoint detection or other methods (am slowly becoming aware of wavelet transformation, etc. but have tons to learn in this area) to identify key shifts in health care ...
6
votes
1answer
733 views

Why does AIC formula in R appear to use one extra parameter than expected?

I'll use an example so that you can reproduce the results ...
10
votes
5answers
645 views

When to use multiple models for prediction?

This is a fairly general question: I have typically found that using multiple different models outperforms one model when trying to predict a time series out of sample. Are there any good papers ...
7
votes
6answers
2k views

Tools for modeling financial time series

What modern tools (Windows-based) do you suggest for modeling financial time series?