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

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Dummy for multivariate time series regression (intercept and slope effect)

I am trying to understand if it is possible to use dummy observations in time series analysis, to split the effect of two or more groups in the model. Assume that we have n observations for 4 ...
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4 views

Time series comparisons: early detection of mismatching series after n points for efficiency

I am doing time series comparisons. I have a set of values (my query set Q) that I need to compare against many other reference sets (R), each of which contains the same number of values as my query ...
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56 views

How to “undifference” a time series variable

I need to "undifference" or "integrate" a time series variable. In its current state, it is twice-differenced (a money market, cash return proxy variable that was I(2) to achieve stationarity). I ...
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86 views

Decompose a time series into superposition of step functions?

Background I have time series data comprising hourly observations of a sensor's readings over a period of almost a year. The sensor records an environment whose baseline measurements should have ...
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51 views

Which distribution to choose when modeling variance of a normal distribution?

I have a simple time series model where there is a single hidden variable $\lambda_t$ which changes over time: $\lambda_{t+1} \sim \mathcal{N}(\lambda_t,\sigma)$. The $\lambda_t$ is then used as a ...
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5 views

Rolling window in time (t) to compute forecasting [migrated]

I want predict using Recursive Method. Each month (t) i need to roll my data window regarding the last month, one month ahead (t+1) ...
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68 views

“Future-independent” smoothing methods (as exponential smoothing)

I'm searching for time series smoothing algorithms, which give "future-independent" results - each next smoothed value depends only on previous data (smoothed or not smoothed), but not on any future ...
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16 views

If peak was higher than normal, why does updated arima model overestimate activity in remaining time series?

I have a number of time series with strong seasonality and I am using auto.arima() from R's Forecast package along with Fourier and dummy/explanatory variables to address the seasonality to make ...
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25 views

Is the R-square valid in a Regression model with Lag values of the Dependent Variables

I was working on some Time dependent data. Due to Client requirements I am forced to use LInear REgression for the modelling instead of Time series regression techniques like ARIMA. In order to not ...
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50 views

What can be inferred from a short multivariate time series?

I have annual observations of 24 variables over 10 years, and I would like to identify evidence of structural change (regime shift). The data pertain to university enrollment & spending, so I ...
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21 views

Significance of recent observations in time series

Let's consider a time series with two variables, one for the time dimension and one for some continuous observation. The dataset being considered is comprised of n ...
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59 views

Forecast Vs Actual accuracy calculation

I have two time series, first is forecasted values (results of some forecasting algorithm) and second series is, actual values observed for same time frame. We are trying to compare both these series ...
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17 views

ARIMA estimate validation through arima.sim

This is out of my curiosity trying to compare time series input to an ARMA model and reconstructed series after an ARMA estimate is obtained. These are the steps I am thinking: Construct simulation ...
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39 views

Bootstrapping confidence intervals in R [closed]

I am a total beginner in using the bootstrap method... At first I calculated the 95% confidence interval myself using R but it is not normally distributed so this would only be a veery rough ...
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31 views

Least squares method for parameter estimation in AR(1) model

In order to estimate parameters $ \mu $ and $ \alpha$ least squares method can be used.This is what I did to find the least squares. $S=\sum_{t=2}^n(X_t-\mu-\alpha X_{t-1}+\alpha\mu)^2$. And ...
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12 views

Correlation parameter with small nonlinear sample like hour / day of week?

I have a dataset of magnitudes Y which gives the scale / magnitude of some event. Each event is also timestamped: Y = [(time1,mag1), (time2,mag2),...] I want to ...
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30 views

Predicting the increase/decrease of number

I have these entries in my database that looks like this: ...
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26 views

Forecasting a ARIMA(1,1,1) model

ARIMA(1,1,1) process with constant term $\mu$ is $X_t=\alpha X_{t-1}+\mu+Z_t+\beta Z_{t-1}$ where $Z_t$ is white noise with mean zero variance $\sigma ^2$. Find one step and two step ahead forecast ...
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Diferrencing vs Moving Average

Moving Average and differencing a series can both be used to remove seasonality. Does the difference of these two lie in the model they are used? Moving Average used in classical decomposition and ...
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13 views

I have to know about the tslm function in r [closed]

I need the full details of tslm function in r beacause I have to know this.Can you please give the explanation about the tslm when we use?,why we use?,how it works?
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How to analyze an inflection point in univariate time series?

I have a univariate time series with 36 data points (monthly data for 3 years). The general trend of this time series is relatively steep decline, but for the most recent one year, it is almost flat ...
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1answer
40 views

Forecast error for AR and MA process

AR(p) process is denoted by: $X_t=\mu+\alpha_1(X_{t-1}-\mu)+\alpha_2(X_{t-2}-\mu)+...\alpha_p(X_{t-p}-\mu)+Z_t$ I don't understand forecast error. Let $\epsilon_{t+l}$ be the forecast error at $l$ ...
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29 views

SARIMA model equation

Can someone please tell me in the book here how is this SARIMA equation obtained? I know that AR(1)=$Y_t=\alpha_1Y_{t-1}+e_t$ Non Seasonal AR(1)=> $Y_t(1-\alpha_1B)=e_t$. My question is what ...
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38 views

Time Series Stationarity

I am confused of why my Dickey-Fuller test is significant (which implies stationarity), while the time series clearly exhibits a deterministic trend?
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18 views

Generalized additive mixed model in R - specifying a fit function

The data in question comprise two response groups (no response vs. stress signal), different individuals, repeated measures ...
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14 views

How to test a time series' serial correlation with ties in R?

I was trying to test serial correlation for a time series measurements (x1,x2,...xn). The problem is that some of them happens in the same date, the time points are ...
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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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Which model to predict air cleanness (air pollution) in daily-basis? [closed]

How hard it is to predict air pollution? My friend is an agronomist: he is doing some research on some small plants. The plants are very sensitive to air pollution in urban areas [need deep ...
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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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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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18 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
60 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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20 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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24 views

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

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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12 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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16 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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26 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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22 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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40 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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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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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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15 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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66 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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15 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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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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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
38 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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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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27 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 ...