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

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timeseries forecasting when datapoints doesn't start at same time period

I have a dataset which has lifecycle information of different products but all the products doesn't start selling in same period or same year/quarter.In this case how should i do time series modelling ...
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Engle and Kozicki (1993)'s Serial Correlation Common Feature (SCCF) test

I have two auto-correlated stationary time series of I(o). I want to look for a common feature in them as per Engle and Kozicki (1993). Specifically I want to see if there is a linear combination of ...
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15 views

multicomparison treatment against 2 controls

i'm new in "r" and in statistics. I have some data that i would like to analize. Just to give you a background of my experiment: 1) i have 4 treatments (ifferent concentration of drugs) 2) i have 2 ...
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Forecasting Call Center Wait time with Unknown Staff Levels

I am trying to forecast the median wait time each hour for a customer to get served in a call center. I know the median wait times each hour and the number of customers who called in each hour ...
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41 views

Constructing Deterministic Trend and AR(1) and Forecasting in R

I am trying to implement/generate a process using arima.sim like this: $Y_t = a + b*t + \epsilon_t$, where $\epsilon_t = \phi\epsilon_{t-1}+\gamma_t$ a AR(1) ...
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Organic vs Paid Attribution Model (Granger)

I'm wondering if there is literature or studies done on how to model organic attribution from paid user acquisition. So the context is, on our mobile app, we have paid installs that we purchase and ...
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36 views

auto.arima Not Minimizing AIC

I simulated a MA(3) process using: ...
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19 views

How to test the relevance of Taylor's rule after the crisis of 2008?

I have a quick query regarding the selection of methodology and test for the empirical relevance of Taylor rule especially after the global financial crisis of 2007 to 2009. I want to capture ...
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44 views

Does stationarity imply absense of upward or downward trend?

I wonder if a time series being stationary implies that there can be no upward or downward trend. It appears to me that such an implication should hold, since in order to be stationary a time series ...
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Adjusting for seasonality doesn't seem to work?

I am trying to adjust my data (stored as ts object in R) for seasonality. I have followed the instructions here [missing link]. ...
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Simulation from Copula and generating the data using simulated residuals

I fitted AR(1)-GARCH(1,1) to two return series u,v of length 500 each. Then, I plugged these residuals (after converting to uniform using PIT command in R) to a copula and got the parameters. I ...
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48 views

Incorporating autocorrelation into forecasts

I have a time series $x_{t}$ which is an AR(1) process with a constant term, e.g. $ x_{t} = c + \phi x_{t-1} + \epsilon_{t} $ How can I incorporate information about the autocorrelation of $x_{t}$ ...
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42 views

ARMA lag selection for ARMA-GARCH models

When I read this group questions about lag selection for ARMA part of ARMA-GARCH models I found 2 different answers from moderator: The use of GARCH and ARMA GARCH estimation process in practice I ...
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31 views

Select and aggregate time series based on selection information of a second dataset

General problem: I have two datasets in r and I do not know how I can calculate information across groups of time series in one dataset based on selection-information of another dataset. The details: ...
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19 views

Conflicting cointegration results due to different lags in Johansen procedure

I have been using two different models for cointegration: Johansen's test and ARDL (autoregressive distributed lag). I guess this example could be extendent for other cointegration models as well. I ...
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15 views

Marginal distribution fitting and copula

I have simulated many pseudo-observations from a nonparametric copula density estimate (for that I used a bootstrap approach). I now want to go back to the original space, but I can't use any ...
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25 views

Outliers detections in time-series

I am searching algorithms for detecting outliers in a time-series data. I see that there are a lot of algorithms and they have an implementation in R. But i don't find any explanation on how they ...
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32 views

Rolling forecasts: training versus forecast accuracy evaluation

Questions: Are rolling forecast examples (like the ones below) only useful for evaluating a model's accuracy, or can a rolling forecast be used to train a model? Are models trained using a rolling ...
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26 views

Question about rolling forecast horizon

I'm trying to understand how the rolling forecast example below from Rob Hyndman's blog works. In the final line of the for loop, is ...
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40 views

Are rolling forecasts more accurate that full-sample forecasts?

I compared the auto.arima forecast checkts below to the rolling forecast fc and noticed ...
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Time Series Modelling With Two (or more) Periodic Components

I'm trying to create a model to predict hourly electricity usage. Looking at the data, it appears that there are three different components that I'm going to want to capture in my model. First, there ...
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33 views

How to implement a multiple regression for AR models (time series)?

Let's say I have the following model: So I have an AR model of order 3, and I want to estimate A1, A2, and A3. I understand how regression normally works for two variables x and y. Also, after ...
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12 views

Time-series forecasting for a 1 year data of monthly data points

I am working on a project where I am required to build a time series forecasting model for forecasting the monthly sales of a company. However, the sample size I have is only for a single year which ...
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31 views

Bayesian Priors Update: Difference in Mean detection

Suppose I have measures of the life span of mice. I know the true expectancy in the beginning of the experiment - 1000 of days and true variance. At some (unknown) point mice begun to be fed by a new ...
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Weighted Box Plot

I have a summarized data in r that looks like the following: ...
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How would you correlate time series (price changes) to a discrete event happening?

I'm not sure what kind of model you would use here... But say you are looking at the price of the S&P 500. Suppose... If the market trends up, then everyone is happy and more likely to spend. ...
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Very different prediction intervals from ARIMA models where MA order differs by 1

I have fit an ARIMA model to a time series with function auto.arima from "forecast" package in R. I wanted to check prediction intervals for robustness by changing ...
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48 views

Forecasting GDP using regression, ARIMA and ETS

I am building a simple model that estimates future change in GDP growth using change in working-age population (%). $$ \Delta GDP_t = \beta_0 + \beta_1 \Delta Pop_{t-1} + \varepsilon_t. $$ I have ...
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Using Mantel test to check spatial independence in time series?

For my analysis in biology, I want to study the effects of several factors (climate, cultivated area...) on insect dynamics and phenology. For that, I have data of insect captures from several traps ...
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What are the various ways to group a big time series (>2000) into few categories and apply one algorithm for each of these groups?

I have ~2000 time series to forecast. Do I need to be able to group them (for example, into 10) or find associations/dependencies between them (tests such as HHG, dCov etc.) so that I can use my ...
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76 views

Finding a pattern in time series data

I have time series data. I am looking for a procedure to find if a particular pattern exists in the time series. To make it more clear, suppose I have a base time series in which the check for the ...
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24 views

how to do preprocessing for univariate time series data

I'm new to the field, and I'm a bit confused about the data preprocessing procedures. The univariate time series data (more than two years daily data) is from retail sector, which contains trend, ...
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23 views

How to model timeseries temperature data?

I would like to model a timeseries consisting of internal temperature data of a greenhouse, collected at 15 min interval and then use it to make predictions in the future. This is how my data looks ...
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32 views

Help in understanding a clustering technique using neural network

I am having difficulty in understanding a technique for clustering and segmentation of biomedical images using the concept of time series. The paper on which the Question is based is : M. Lacomi et. ...
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Test for significance of peaks (maximum) in time series

I have a time series of values, something like this: ...
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32 views

Testing for discords in seasonal time series data

I'm trying to find a way to detect discords in seasonal data. I have an algorithm that can select the most likely sub-sequence to be a discord, but what I'm missing is an actual test. I know that ...
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19 views

Grouping data in a multiline chart mean + outliers

I have an existing multi-line graph that displays time series data about success percentages of nodes in a cluster in 5 minute intervals, there are more than 50 nodes in the cluster and the way this ...
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112 views

Testing difference between two portions of a time series with Chebyshev Theorem

I have a time series which presents two different patterns during time. These patterns are related to two different events that happen during the experiment. I can manually select the temporal ...
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32 views

Standard deviation vs Stardard error of sample mean

I am currently attempting to analyze some data, and it has been a few years since I have taken a statistics course, so I am a bit rusty at this stuff. I have a times series of 12 different sample ...
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How can stacking be used to combine time series forecasts?

In classical classification problems, stacking means that some model is trained from the results of a number of previously learned models. Cross-validation is commonly used here. How would you do this ...
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1answer
56 views

How to identify best Model for univariate time series data?

I have a time series data- 53.97 63.32 57.06 60.27 69.46 75.08 78.31 73.28 85.84 69.34 62.57 60.11 55.63 47.29 61.22 58.46 66.26 59.71 51.12 39.36 51.89 ...
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58 views

Time Series Analysis vs Linear Regression for GDP data?

I am trying to build a simple econometrics model that uses urban population, total factor productivity among other things to predict future GDP of a country. First I approached the problem by using ...
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44 views

Predict (un)employment variables - very small dataset

I'm new to econometrics (familiar with ML, Python, Data Visualization). I really have no clear idea what model should I use in order to predict (un)employment variables for 2015-2016 (potentially ...
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23 views

Time-weighted Pearson correlation

I'm trying to calculate time-weighted Pearson correlation as described in http://goo.gl/HoqwI7 The coefficient is given by $$\rho_t(X,Y) = \left ( \frac{1-r}{1-r^N} \right ) \sum_{i=0}^{N} r^{i-1} ...
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Hidden Markov model - Time Granularity

Is Hidden Markov model sensitive on time granularity? I mean if I train HMM parameters on dataset which time granularity is 1 minute. May I use the transition matrix and emissions distributions for ...
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27 views

Difference between AR(1) lagged models with exogenous variables versus ARIMA(1,0,0) models with exogenous variables

Assume a general time series $y_t$ for $t = 1, \cdots, T$ and a sequence of known exogenous variables $x_t$. Consider the two options: Fitting the parameters $a$ and $b$ of the lagged model $y_t ...
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28 views

Holt-Winters vs. comparison to history

I have a timeseries with daily and weekly seasonality that I want to check for anomalies (on data as it comes in live). I could use Holt-Winters forecasting, or I could just compare the data with the ...
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Forecasting Adjusted Time Based Cohort Values Based on Variance

I'm trying to forecast the distribution of sales for a three week cohort with adjustments for the remaining weeks made from the past weeks results. The basic approach would be to adjust the next weeks ...