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

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Why does NSDIFFS (R forecast package) never show seasonality?

I've been using the EViews statconn DCOM interface to loop a large number of series from FRED through the nsdiffs(test=c("ch")) function in the forecast package of R to examine what percent of them ...
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17 views

How can we compute cumulative change rates for time series data?

Take the annual precipitation data for some area from 1960 to 2008 as an example. How can we compute cumulative change rates for such data?
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7 views

Applicability of Hilbert-Huang Transform for linear trend analysis

I have a question about the applicability of the Hilbert-Huang Transform / empirical mode decomposition (HHT/EMD). Suppose I have a time series dataset in which there is probably an N-year periodic ...
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13 views

Time series model of prevalence

I have a collection of samples from which I have estimated prevalence on an annual basis using a logistic regression model. The response variable is whether or not the focal species was present in ...
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18 views

Dynamic Time Wrapping for finding divergence in timeseries data

I have the time series information of various S&P500 sectors. I need to find which sectors are outliers and diverging from the bunch of sectors. As you can see in image below, in month of October, ...
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1answer
11 views

Selecting the best (or more suitable to the user/client) output from a set of forecasts

I have approximately 3000 products for which I have to forecast in every, say, 2 months. I have the code in place for different forecasting models such as ARIMA, forced seasonal ARIMA, STLF etc. Now ...
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13 views

Comparing 2 time series in R

I was wondering what kind of tests one would use to compare these two time series. The first data set(in percentages) are results from a weekly survey that asks a YES/NO question on whether someone ...
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1answer
71 views

Are time series methods only good for forecasting?

Many time series methods are oriented solely in terms of forecasting (e.g., ARIMA). However, it seems like a growth curve modeling framework (i.e., random coefficient modeling) can do virtually ...
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26 views

Can you generate confidence intervals for time series ETS forecast components?

Suppose you fit a time series with the ets function from the forecast package in R: ...
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12 views

Log returns and ARMA-GARCH models

I try to model currency rates volatility using GARCH models through the RUGARCH package in R. Starting from the observed currency rate series, I compute the log-return through: ...
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61 views

How to calculate probabilities based on cumulative of time series?

I am trying to do predictions on plant growth based on cumulative of time series data. Unfortunately I am not a statistician, just a programmer tasked with writing the application that does this (PHP ...
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14 views

Bootstrapping - Variance of Time Series with Micro-level Data

I have micro-level (individuals) time series data and I am able to calculate some aggregate statistic for each time period. The data is not a panel, so each month is a different cross-section of ...
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27 views

State space model with regression effects

I'm trying to show the following (exercise 3.11.4 from Durbin and Koopman (2012)): Show that the state space model defined by $$ y_t=X_t\beta+Z_t\alpha_t+\epsilon_t\\ ...
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2answers
36 views

Forecasting product of two time series with correlation

I am trying to forecast the product two time series. That is, given $\{x_t\}_{t=0}^{T-1}, \{y_t\}_{t=0}^{T-1}$, forecast $x_T\cdot y_T$. The two time series have minimal but nontrivial correlation ...
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1answer
37 views

Asymptotic distribution for moments of gaussian distribution

Is there a way to find the asymptotic distribution for the moments of Gaussian distribution? More specifically, say you have $X_1, ..., X_n \sim N(\mu, \sigma^2)$. For a moment $m_{n, k} ...
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8 views

How to recombine seasonally decomposed stl components in R [migrated]

I want to recombine the seasonal components to the seasonally adjusted components for a time series that is decomposed by stl. For example: ...
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24 views

How Can I Model Multiple Short Time Series Samples?

How Can I Model Multiple Short Time Series Samples? For example, let's say I have a new subject each month, and I measure each subject every day for the entire month. I then want to model these ...
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10 views

How to analyze the interaction of temperature and PM on mortailtiy? [closed]

library(dlnm);library(mgcv);library(splines);library(tsModel) the model for main effect,(R code) ...
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19 views

How to analyze the effects of air pollution separately for the warm season and the cool season

In the model for main effect, we used the R code: ...
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2answers
143 views

Times series analysis vs. machine learning?

Just a general question. If you have time series data, when is it better to use time series techniques (aka, ARCH, GARCH, etc) over machine/statistical learning techniques (KNN, regression)? If there ...
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53 views

Generalized Linear Models vs Timseries models for forecasting

What are the differences in using Generalized Linear Models, such as Automatic Relevance Determination (ARD) and Ridge regression, versus Time series models like Box-Jenkins (ARIMA) or Exponential ...
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1answer
34 views

First remove seasonal trend or long-term trend in time series?

I have a time series (quarterly data) which has both a long-term trend and seasonality. Taking seasonal differences will make the series stationary, according to the Augmented Dickey-Fuller test. On ...
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14 views

Can we conclude anything about two time series which have the same order of integration?

Two time series, when tested for stationarity, were found to be of the same order of integration. Can we conclude anything about the two series?
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29 views

Causal Impact R Package

We’re doing some advertising tests with test and control groups very similar to the example in the Google Research Causal Impact publication except we’re doing state tests and not DMA. I just have a ...
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1answer
31 views

Getting standard errors for the intercept/mean in R-function ARIMA()

If i understand correctly, the ARIMA function produces an estimate for the mean of the process instead of the intercept. It is possible to transform the mean into the intercept: mean= ...
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26 views

Definition of AIC in ARIMA() function in R?

I wonder how the Arima() function in R computes the AIC. Applying the standard formula AIC= 2*k - 2 LN(L) (with k number of parameters and L maximized value of likelihood) doesn't reproduce the ...
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2answers
79 views

Detecting Bimodal Distribution

I have histograms of audio signals where they have bimodal "normal" distribution. What I want to do is to detect these subpopulations inorder to have a threshold, this is meant to divide the values ...
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29 views

Similarity measure for time series

How can I recognize patterns with different sizes in a time-series? Imagine that I have a template pattern and I need to find that pattern in a symbolic representation of the time-series (stock market ...
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7 views

Change Attribution in Multi-variate time series

Consider a time series X which is a function of other time series A, B and C. The exact nature of the function is known. What I want to do is to identify a change in X and attribute the change to its ...
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37 views

Statistical Analysis of a set of numbers to predict final outcome

Suppose I have a collection of sets of numbers, lets say that each set has twenty five numbers in it (one for each day in a twenty five day period). Lets say I have 200 of these sets. Are there any ...
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1answer
24 views

What Grouping Method To Determine Average Over Lifetime?

I have the following data: When individual 'x' joined a company. As the data is limited to 2 years I do not know the start date of every individual. When individual 'x' left the same company. If this ...
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1answer
31 views

Initialize AR(p) process by using Arima.sim

Hi I am currently trying to simulate an AR(4) process $y_t=0.67y_{t-1}-0.51y_{t-4}+\epsilon_t$ given that the initial value $y_1=1,y_2=2,y_3=3,y_4=4$ and $\epsilon_t\sim N(0,1)$. My code is given as ...
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21 views

How can i do time series forecasting with missing data

I am relatively new to time series forecasting, I have worked previously with continuous data at regular intervals successfully, Now I have a data set with missing values, for example look at the ...
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1answer
62 views

R forecasting, flat forecast

I’m trying to produce a hourly, daily forecast for revenue in R. I set seasonal periods to 24, for 24 hours, and 365.25 for days in a year. I attached the fit vs actual plot and the forecast produced ...
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21 views

Is it possible to form a parametric representation for Mean Square error?

Consider a stable causal, single-input/single output, linear time-invariant, discrete-time system. The noisy output is $y[n] = \sum_{i=0}^{p-1} c_i d[n-i] + w[n]$ where $c_i$ is the real-valued ...
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17 views

How to model and forecast spike cycles in a time series

I’d like to model repeating peaks of various periodicity of a time series as a curve. Here’s the general scenario: A device under measurement experiences reasonably regular voltage spikes every N ...
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23 views

How to decide the p and q for GARCH model?

My question is simple. When shall I stop when trying the value for p and q? I have got the loglikelihood from ARCH(1) to ARCH(10). It's increasing. And then I tried GARCH(1,1), GARCH(2,1) etc. The ...
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16 views

arimax function error [closed]

I have been stuck with an error returned by the following R command: ...
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1answer
17 views

How could I use VAR model for nonstationary series?

I have five independent variables: oil (stationary at level), f (stationary at level), k ...
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1answer
42 views

Problems with seasonality removal

My problem is similar to this one from stack overflow: http://stackoverflow.com/questions/23568275/cannot-remove-time-series-seasonality I'll provide some data and make it more detailed. Please keep ...
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22 views

Time series classification and auto correlation

I am trying to understand time series data and data mining. I am trying to classify EEG data set. The classes are known in advance for the data set and the algorithm is trained on the example data ...
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22 views

Interpret partial autocorrelation

I am learning about ACF and PACF graphs. I am not sure I understand how to interpret the one I got for my data.
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2answers
23 views

What time series type analysis in R should I use?

I'm trying to determine what type of analysis I should use in R for my data/question. I'm thinking some type of time lag or time series analysis? My question is how the duration of a behavior ...
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2answers
45 views

forecasting sharp seasonal peak in time series

I have time series data on a daily level over the past 4 years. What is clear from examining past data is that there are two very clear peaks in the time series around the same time of year (they ...
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17 views

Imputing missing values of predictor for use in Regression Models

I have a panel data set that extends from January 2013 to July 2014. The response variable is complete for the entire period, however all of the predictor variables have values only up to June 2014. ...
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1answer
44 views

Can a Moving Average be used as a dependent variable in a regression model?

I have a time series I want to use as a response in a regression model. The problem is that I suspect that the changes in this variable could be due to sampling error. As a result, I created a moving ...
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8 views

Lumping of data

I have some time series $X = (X_t)_{t\leq T}$ that range over a finite set of values. For each $X_t$ let the next different value $X_{n(t)}$, that is $n(t)\geq t+1$ $X_{n(t)}\neq X_t$ and $X_s = ...
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1answer
54 views

Timeseries Analysis

I have the weekly time series data from 2011 to 2014 with 6 variables(Gross_Revenue,Attendence,Enrollmentcount etc..) and its having seasonality.I want forecast the Gross_Revnue for 2015 1st 15 weeks ...
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22 views

Prediction period is coming wrong in the HoltWinters in R

I am Using Holt-Winters model for the forecasting. Below is the way I am proceeding: ...
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15 views

Does forward filtering result in OLS estimate for each time point?

I'm learning about dynamic linear models and was trying to think about the relationship between GLS and forward filtering (Kalman filtering where the state is the vector of parameters). Here's my ...