Time series are data observed over time (either in continuous time or at discrete time periods).

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Can I difference after fitting a time series regression model?

Suppose that I have a time series that exhibits a notable trend, and I want to test a hypothesis that a second variable is related to that trend. I fit a linear regression model with that second ...
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25 views

ets() or stlf()

Every where I read, experts suggested to use ets() to better determine alpha, beta, gamma ...
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35 views

How to compare difference between two time series?

I am working on my thesis where I'm examining how strong emotion people show to different events. My problem is (1) that I have VERY little experience with statistics and math, so I'm kind of lost ...
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31 views

Forecasting agricultural commodity prices with R

I would like to create a predictive model in order to forecast the price of an agricultural raw material. I got time series for the prices and the production of this raw material, and also for the ...
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10 views

Derive minimum positive rate of change for co2 data

I have CO2 (in parts per million) data of a closed room. The CO2 data is recorded along with timestamps. Typical difference between two samples is around five minutes. My aim is to find occupancy of ...
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1answer
36 views

Given time series data, how to model the frequency of someone changes his job?

I am given a time series data vector (ordered by months and years),which contains only 0s and 1s. ...
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15 views

Testing for heteroskedasticity of time series in R

I wish to test my time series data for volatility clustering, i.e. conditional heteroskedasticity. So far, I have used the ACF test on the squared and absolute returns of my data, as well as the ...
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1answer
23 views

ARIMA modeling with more than one Categorical Variable

I am using auto.arima for forecasting. I have more than one categorical variables having more than one level. My questions are : Do I need to do dummy coding ? ...
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11 views

dynamic probit model

I want to know if you can helpme I have a binaray response yt take values 1,0 and a covariate Xt continua and I have to estimate the parameters of the model using maximun likelihood method ...
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10 views

Pattern and most influential parameter detection.

I need some advise to approach the solution. Here is the question. Background: The growth of bacteria, Gb is dependant on 4 factors W, X, Y, Z where W and X --- can take any integer value from -10 ...
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24 views

Interaction in time series analysis

I have three different physiological variables--heart rate, respiratory rate and blood oxygen saturation, each as a time series. I am trying to study the interaction between the variables as they ...
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19 views

How to approach time series regression with one continuous variable and one “ almost Boolean” variable?

I am working in R with daily time series data and have daily observations of two variables. The first is continuous. The second is zero for every day except one, in which it is a number (I'm not sure ...
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1answer
17 views

Stationarity consideration in ARIMA using KPSS test

I have data, which I am sure has a downward trend. I am trying to forecast this data using ARIMA and I want ARIMA to consider the trend when it is forecasting. The first step in ARIMA is to ...
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1answer
71 views

Time series with negative data in R [closed]

I have data for forecasting like, here negative value is actual data ...
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12 views

Tail index using hill estimator in R [migrated]

As part of my data analysis (of heavy-tailed data) I wish to calculate the tail (for both left and right) indices of around 100 time series of company returns. My data is stored in a large zoo object, ...
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3answers
64 views

Intervention Analysis Coding in R TSA Package

I am studying intervention analysis in time series with the Cryer and Chan book and am looking at trying to understand how to code the step response interventions. One question I had is how to ...
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0answers
9 views

Correlation between variables over time

I have two variables over two different years. Week by week pageview numbers in 2013 and 2014 and week by week # of posts in 2013 and 2014 (number of articles posted). I'm trying to find out if ...
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18 views

Outlier treatment in Vector Autoregression (VAR) Model using vars package in r

I have the same problem as the following post, but I have more samples and the index of the outlier is known. Outlier treatment in Vector Autoregression (VAR) Model I tried deleting the outliers; ...
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26 views

Demand Forecasting : Montecarlo Simulation

I am trying to build a demand forecasting model for human resource team. I have thought of using monte carlo simulation method to do it. Is it the right technique for it? Has anyone used it to ...
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61 views

F-statistic value is too high for my model

Number of obs = 501 , Method: Fixed-effects regression Number of groups = 101 F( 10, 100) = 3422.31 , Prob > F = 0.0000 , ...
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1answer
54 views

Backshift operator property not clear

In my introductory book on time series analysis the backshift operator $\mathbf{B}$ is introduced using the following definition: $$ \mathbf{B}x_t=x_{t-1} $$ Then the author sets off to derive some ...
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26 views

Bayesian method for computing credibility interval for correlated time series

I'm studying a stochastic process generated by simulation using two different methods. In the first, the waiting time between events can be shown to be exponentially distributed. To model the ...
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29 views

Can seasonality be detected / explored with principal components analysis?

I have a rainfall data consisting of around 95 years for the rows and twelve months of the year for columns. So this is a 95x12 matrix, not a column vector. Can I derive any idea about the months to ...
2
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1answer
109 views

Question with MLE

I'm having some problems with this question, and was hoping someone here could help. Let $X_1,\ldots,X_2$ be $n$ determinations of a physical constant $\theta$. Consider the model $X_i = ...
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30 views

Kalman filter transition matrix

Hi guys I am trying to writ e a code on python to correct forecast data using Kalman Filter. I am following the equations and recommendations in this link : ...
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126 views

Under what circumstances is an MA process or AR process appropriate?

I have a very basic question. Please let me know if this has been asked before, but in my defence I haven't seen it on Cross Validated. I understand that if a process depends on previous values of ...
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1answer
53 views

How to forecast time-series bounded by [0,1], i.e., forecast relative frequencies?

I am working with time series values which are all in the closed interval [0, 1]; these values represent relative frequencies, i.e., empirical probabilities. I would like to create a model such that ...
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1answer
31 views

Combinef in R HTS package- constrain to keep forecasts positive?

When using the combinef function from Rob Hyndman's very useful hts package for forecasting hierarchical and grouped time series, there does not seem to be a way to constrain the optimally combined ...
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16 views

Specifying a glmm for panel data

I'm trying to predict counts based on variables sampled on a monthly basis as well as a few that are not related to time. In several places I've read that the MCMCglmm package in R would be ...
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32 views

Multivariate Time Series

I am trying to learn multivariate time series using R. I have two time series and I want to see if I could use one of those to predict the other one, and after that check if the model holds or there ...
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1answer
31 views

Simulating non-causal time series?

I'm looking into the possibility of using a non-causal time series filter for some data. The goal is filtering (for the purpose of anomaly detection). However, this is not particularly relevant. I'm ...
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1answer
22 views

Separating and identifying long and short term effects of statistical variables

A quick general question: In a practical setting, what's a good way to separate out, and then comment on, long term and short effects in a model? I had thought a good way to do so would be to include ...
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4answers
146 views

Can a trend stationary series be modeled with ARIMA?

I have a question / confusion about stationary series required for modeling with ARIMA(X). I am thinking of this more in terms of inference (effect of an intervention), but would like to know if ...
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2answers
56 views

Interpolating time series

what are best ways to interpolate time series? I have three data points(1980, 1990 and 2001) and I need to interpolate them. Using R na.approx doesn't seem to be what I need since the data I need to ...
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2answers
51 views

cointegration - same thing as stationary residuals?

So I'm aware that cointegration means there is some linear combination of the set of variables that is stationary. So, if you do a regression and find stationary residuals, can you just immediately ...
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73 views

Avoid negative results in Holt Winters forecasting

I understood that Holt Winters forecasting may results in negative values due to trending. I did reduce trending component value, but still forecast values are negative territory. Our data set will ...
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35 views

Turning general regression into time-series prediction

Suppose you have a general regression model, which behaves like a black-box to you. All you have is a $\ \ \text{train}(X,Y) \ \ $ function, where you input your predictor matrix $X \in \mathbb ...
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13 views

What approach to use for attrition analysis?

I am trying to perform an attrition analysis on a company with an average size fo around 250 to 300. I have monthly attrition data for the last 30 months or so. Now if i want to go for a predictive ...
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2answers
57 views

What are some methods for generating simulated time series data for use in modeling?

I have a data set which consists of 50 observed years for which I have date and inflow values between a river and a reservoir. The data is formatted as follows: ...
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2answers
71 views

Best way to test for co-occurrence of measures

I have some data with temporal measures over time. I'd like to test whether two binary variables co-occur more often than chance would predict, and I'm wondering the best (simple) way to do that. The ...
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Methods for measuring snowball effects in a “complete” longitudinal dataset

I'm looking for ways to test for "cumulative advantage" effects in a longitudinal dataset (see image) I guess the data set is principally similar to this: http://www.caldercenter.org/whatis.cfm , ...
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3answers
62 views

Gaps in time series and time series validity

After doing some reading on CrossValidated, I understood that we can use "imputation" techniques to fill in the gaps (if they are random). But I am not clear on following questions: How many ...
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8 views

custom axis labels plotting a forecast in R [migrated]

I'm trying to get some sensible labels on a forecast. Here's my code: ...
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21 views

representative baseline for time series-like data

I have data for several individuals that takes the following form over time. Many individuals are flat over the course of a year, meaning their measurements stay roughly the same. Others have a peak ...
3
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1answer
59 views

How do you create variables reflecting the lead and lag impact of holidays / calendar effects in a time-series analysis?

I am working on a time-series project in which I am forecasting the daily activity of something (let's call it 'Y') based on three years of historical data. I know that Y is affected by calendar ...
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10 views

Comparing two time series over different periods of time

I have two time series that I wish to compare their movements. The issue is that one time series is daily data since 1920 and the other is daily data since 2008. When plotting them separately over ...
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15 views

How to fit two or more datasets with different occurence for regression

I want to run a regression in R with different datasets. The question is whether stock performance (daily log return) is influenced by factors like interest rates (the one set by fed or ECB), size of ...
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1answer
37 views

Time series: Stationary R-squared vs R-squared (Forecasting in SPSS)

I would like to check how two time series are correlated - how one of them predicts the other one. I have data for 27 years. I conduct my analysis in SPSS (Forecasting command, ARIMA) and I get: ...
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20 views

linearity of a time series

I am currently trying to correct forecast data using Kalman filter (python). I do not know where to start. I wanted to know how can I do a test to Know if my time series is linear or non linear? Is ...
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16 views

Regression line fit for linearly increasing data with manual reset

I've a linearly increasing time series dataset of a sensor with value ranges between 50 to 150 on which I've implemented a simple linear regression algorithm to fit a regression line, and I'm ...