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

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Best practice for ADF/KPSS unit root testing sequence?

I've been quite confused by the various unit root testing strategies recommended in the literature, so I was hoping others may have some advice on the best way to proceed using ADF and KPSS tests. ...
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26 views

predicting Air cleanness (air pollution) in daily-basis

How hard it is to predicting the cleanness of air ? My friend is an agronomist, he is doing some research on some small plants. The plants are very sensitive to air pollution in urban area (need deep ...
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15 views

How to estimate the percent of the variation of a time series explained by another time series (non-stationary)?

I've been learning about time series analysis because I want to understand how much groundwater level changes in an aquifer affect land subsidence (land sinking). I have two time series: (1) ...
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14 views

How to detect the time dimension in a candidate time series?

I am trying to build a quantitative method for detecting that a multivariate dataset is in effect a time series, and for estimating its parameters. The Runs Test would be used for quantifying the ...
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12 views

Clusters as input for classification

I'm currently performing clustering as a batch job and then in real time I'm assigning new points to cluster whose centroid is closest to new arrived point. The other approach that I see is to ...
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1answer
47 views

Hourly predictions using time series

I'd like to build a model based on time series. I have a dataset with records every 30 minutes for three months. What is the difference between modeling these data with the following kinds of ...
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13 views

cumulative uncertainty with time series predictive model

So I have a time-series with a set of variables a, b, c... and another measured variable y. What I do is using the initial state of a,b,c and y (at t0), I predict what y "should" be at the next time ...
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23 views

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

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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19 views

How can we compute cumulative change rates for time series data? [on hold]

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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11 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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15 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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24 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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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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16 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 ...
4
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1answer
74 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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65 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 ...
2
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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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11 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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22 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
146 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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56 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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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
82 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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39 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
33 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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23 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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27 views

Guidelines to estimate using MLE from this definition of error function

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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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 ...