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

learn more… | top users | synonyms

0
votes
0answers
6 views

Can you specify AR(p) structure for cyclic spline in mboost?

Suppose I fit want to fit a boosted GAM using mboost:gamboost to time series data. Is it possible to specify an AR(p) structure for the cyclic component following a ...
0
votes
0answers
6 views

How to build the A MAtrix for a A-Model(Amat) after a REDUCED VAR in R

i have a doubt in how to build the A MAtrix(Amat) for Estimating a SVAR model in R: I estimated a reduced VAR with the GDP, interest rate and inflation variables . With the economic theory and the ...
0
votes
1answer
23 views

Why are my time series predictions constant?

I have a time series of log returns (from 2007 to 2012). I need to predict the $t+1,t+2, \ldots t+1510$ log stock returns. I have therefore done the following coding in ...
6
votes
3answers
126 views

Best method for short time-series

I have a question related to modeling short time-series. It is not a question if to model them, but how. What method would you recommend for modeling (very) short time-series (say of length $T \leq ...
1
vote
0answers
10 views

Correct df in longitudinal linear mixed model?

I am having trouble understanding how to correctly apply a linear mixed model to my data to measure the effect of wifi exposure. 4 beehives contained sensors collecting data on temperature (DHT22_t, ...
0
votes
0answers
7 views

An observation too short / missing data in panel

I have a panel data set with 7 lines or concepts from 1948 to 2013. However there is an 8th concept that I need that is only from 1993-2010. Is there a way in which I could estimate this variable's ...
1
vote
0answers
7 views

How should I represent validity of a population prediction?

I am looking to report on the validity of a predictive population model for which I only have the output prediction p(t) and the time for which the prediction ...
0
votes
1answer
26 views

Time series with multiple subjects and multiple variables in R

I'm having trouble finding a time series technique to deal with a data set I am working on. It contains multiple subjects and multiple variables, not all of which will likely be part of the time ...
0
votes
0answers
10 views

Forecast Error Variance Decomposition with restricted VAR model

For conducting Forecast Error Variance Decomposition (FEVD) on a restricted VAR model I use the fevd method in the package vars ...
0
votes
0answers
11 views

Obtaining adjusted proportions in repeated measurements design

I want to compare the blood pressure trend in a population. I have 1000 individuals, roughly and they have been examined from 2003 to 2012. Some individuals, but not all, have been examined several ...
0
votes
2answers
37 views

fitting a cubic polynomial to a trend component of time series

I have 295 observations of two variables, of which here are a few: ...
1
vote
2answers
42 views

How can I model a binary outcomes in time series using logistic regression?

My data has a binary outcome (attack or not attack), day (20 day in repeated measured design) and some covariates (nestling’s movement). The objectives of my experiment are testing the effect of time ...
0
votes
0answers
16 views

Joint distribution R [migrated]

I have a list of 10 stocks, with each having a time series of log returns (AIG, JPM,...). I have calculated the log returns for each of the stocks as follows: ...
0
votes
0answers
26 views

Find distribution of Bus arrival time

I am currently working on a problem in my research which can be modeled into the following question: Let's say I have a rich dataset with values for the variable $A$ which is equal to ...
0
votes
0answers
5 views

how to implement link anomaly method for discovering emerging topics [on hold]

We are trying to do a project to discover emerging topics in social network via link anomaly method. But we are not knowing how to implement this.
4
votes
2answers
146 views

Do you see trends in my residual plots?

Do you see trends in my residual plots? These residuals plot show the standardized residuals against fitted values, origin period, calendar period, and development period. The patterns in any ...
3
votes
1answer
43 views

Residual based bootstrap autoregressive series in MATLAB

I have defined the model as follows. Let $$y_1 = 0$$ and $$ y_i = \alpha + \beta y_{i-1} + \epsilon_i $$ for $i_2\ldots i_T$, where $\alpha$ and $\beta$ are the estimated coefficients and ...
0
votes
0answers
4 views

simulating AR(1) process for N variables in MATLAB [migrated]

I am quite new in programming, can somebody tell whether the following approach is correct in matlab? clc; clear; N = 15; T = 221; alpha = randn(N,1); % normally distributed intercept as ...
3
votes
1answer
31 views

Help understanding how the cointegration equation for VECM models are derived

I am learning about Vector Error Correction Models from Sean Becketti's "Introduction to Time Series using Stata". While I know how to run the Stata commands to estimate the VECM, I have no idea why ...
1
vote
0answers
29 views

Using seasonality in a model

Suppose you are modeling sales of a prepackaged good and suppose there are seasonal periods in the time series. Also suppose that the brand of the prepackaged good you are modeling comprises the ...
1
vote
0answers
36 views

Prediction based on multiple time series - Python

I have 3 predictors and 1 variable that represents ground truth. They all are linked time series. My purpose is with the 3 predictors to try to forecast the ground truth data. For example : ...
1
vote
0answers
28 views

How to compare and cluster sets of daily time series?

I have multiple dataframes each representing traffic speed for each day of the year (366 dataframes for 366 days of the year). The raws of the dataframe are timestamp from 00:00 to 23:55 at 5 minute ...
6
votes
2answers
111 views

Analyse ACF and PACF plots

I want to see if I am on the right track analysing my ACF and PACF plots: Background: (Reff: Philip Hans Franses, 1998) As both ACF and PACF show significant values, I assume that an ARMA-model ...
2
votes
1answer
40 views

Bootstrapping at group level (time series data)

I have time series of continuous measurements of two different variables $x(t)$ and $y(t)$ measured at times $t_i$. I measured those variables for different subjects (with different characteristics) ...
5
votes
2answers
68 views

What's the model representation for the first difference of a local level model?

This is my first exercise for space state models and I've a few questions I'd need to resolve before I actually start doing the exercise. Unfortunately, I'm self teaching (I have no professor to ask) ...
2
votes
0answers
17 views

How to evaluate a Bayesian forecast?

Suppose that I have a predictive posterior, which is an attempt to predict some one-step ahead forecasted value $\hat{y}_{T+1}$. How do I assess if my posterior has done a good job or not? If we had ...
0
votes
0answers
27 views

How to handle autocorrelated residuals (and independent variables) in multiple regression? [closed]

I am trying to carry out multiple regression for an air pollutant (dependent variable), and weather parameters- wind direction, wind strength, rain, air temperature, relative humidity (independent ...
0
votes
0answers
19 views

Comparison of Non-Stationary Time Series Trends

I am trying to compare two readings of the same occurrences from two different sources, forming two time series. I would like to assign a metric to their similarity/dissimilarity, but the method I am ...
1
vote
0answers
23 views

Suitable model for predicting mean output over time

I have 3 years of yearly historic data on the results of a harvest (of truffles) from multiple different areas involving multiple individuals harvesting in each area. The dataset contains: name of ...
3
votes
1answer
52 views

Is it possible to simplify linear regression if one of the variables does not have errors and is equispaced?

Specifically, I have a time series with fixed $\Delta t$ of the following form: $x_0, x_1, x_2, ... x_n$ and $t_0, t_1, t_2, ... t_n$ where $t_{i+1}-t_i =\Delta t $. I'm interested in the improved ...
3
votes
0answers
30 views

Maximum value of d in ARIMA model

I am trying to model a data series using ARIMA model. The series seems non stationary because the acf decays very gradually.Even after differencing two times, the values of p and q are coming as high ...
2
votes
0answers
24 views

Regressing nonstationary on stationary variable

I am trying to empirically estimate the coefficient for the Okun's law as a relationship between output growth and unemployment. I am using the simple gap version, where I regress real output growth ...
1
vote
0answers
26 views

Get groups in time series with categorical data in R for use in gts

I have sales data organised in a table with 6 columns (4 for the location and type data, and 2 for the dates and the quantity sold), and 24 rows for each category representing the sales over 24 months ...
4
votes
2answers
82 views

AR(q) model with F-test

I am wondering that if we have an AR($q$) model for time series: $$X_i=\beta_1X_{i-1}+..+\beta_{p}X_{i-p} + \beta_{p+1} X_{i-p-1} +...+\beta_{q} X_{i-q}+\epsilon_i,\epsilon_i \;\text{iid}\; ...
-1
votes
0answers
11 views

KFAS example won't run [migrated]

I tried to get the example on page 13 of the KFAS manual to run but no luck. Anyone have any ideas? ...
0
votes
1answer
58 views

Statistically Approximating Clicks From Limited Data

Assume a business started in January 2014. I have the following daily data (from June 2014 to December 2014): 1. Number of people who joined the website; 2. Number of people who left the website; ...
1
vote
1answer
24 views

How to remove level shifts and pulses from time series?

I am interested in describing seasonal patterns in several time series and then seeing if they are related. My approach is to fit regression models with an indicator variable for each season which ...
0
votes
0answers
14 views

MANOVA on autocorrelated variables

I am trying to find out if signals 1 and 2, can explain signals 3 to 10. All signals are continuous and time-varying and are rather strongly autocorrelated. Signals 3 to 10 (my dependent variables) ...
1
vote
1answer
35 views

Using monthly product usage data to predict customer churn

I've been reading tons of papers detailing methods on predicting customer attrition, but none of them have mentioned using product usage data over time. We keep detailed logs of how many times User A ...
0
votes
1answer
39 views

What is the auto-covarriance of a stationary AR1 process?

Say a stationary AR(1) process is given by: $$ X_t = c + \phi X_{t-1} + \epsilon_t $$ where $ \epsilon_t $ is a white noise process with zero mean and constant variance $ \sigma^2 $. Wikipedia ...
2
votes
1answer
68 views

Finding occurrences of specific patterns in time series

I have to locate occurrences of Cyllinder, Bell and Funnel patterns in univariate time series $X$ of gamma-ray sensoring. This is a specific case of the general CBF synthetic problem found in a few ...
0
votes
0answers
13 views

Generating randomized time series from discontinues historical data

I have some historical data from microbiological sampling, which are not collected continuously. For example historical data are in 2008/01/12, 2008/03/25, 2008/5/30, 2009/07/05, 2009/07/29.For ...
4
votes
2answers
82 views

Testing if frequency of event occurrences over week days is uniform

I’m trying to figure out if there’s a calculation that can be done to show you have enough data to draw conclusions on trends within the data set. I have made an observation on a class of equipment ...
5
votes
3answers
63 views

How to take advantage of multiples series with the same behaviour for forecasting?

I'm quite new to statistics and forecasting, and I have to build a model to forecast monthly sales of different related products in a bunch of cities. Seasonal ARIMA seams to be a good model for ...
1
vote
0answers
15 views

How is GARCH's p estimated in software?

From what I know, the GARCH(p,q) model is estimated via MLE and through an iterative process. Let's say if i wanted to recreate a GARCH(1,1) parameter estimation with excel solver (through maximizing ...
0
votes
0answers
27 views

what is a stationary process?

I just started learning time series and this notation confuses me. For a ARMA process, phi(B)X=theta(B)Z according to some notes I found online, the criteria for the process to be causal is where ...
5
votes
2answers
118 views

How to use Pearson correlation correctly with time series

I have 2 time-series (both smooth) that I would like to cross-correlate to see how correlated they are. I intend to use the Pearson correlation coefficient. Is this appropriate? My second question ...
2
votes
0answers
27 views

How can I predict based on several time series of many different projects?

I want to predict the time that a client takes to pay for a service that has already been received. We are talking about a construction company, so the payments are always overdue since the company ...
1
vote
1answer
47 views

Comparing Time Series Forecast Models

I'm to write a short report on Time Series forecast comparison. I'm a beginner in the field. I want to investigate how one chooses which model is better than the other based on the forecast results. ...
0
votes
0answers
9 views

Bisecting K-mediods [duplicate]

Is there an algorithm like Bisecting K-mediods and what would its advantages/weaknesses be? It seems to me that it could be used well in combination of Dynamic Time Warping for clustering time ...