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

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minimize large time series dataset but keep its shape

I have a large time series dataset. My plan is to minimize it somehow so I can work more efficiently with this dataset while the "shape" of the data will be retained. I've read about moving averages ...
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33 views

Exact maximum likelihood estimation of MA(1)

I want to calculate the MLEs of the MA(1) model and for this purpose I have written the exact likelihood for the same. I built a programme in R for the log-likelihood, but it seems some problem in it ...
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Lagged independent variables in economic analysis

I am trying to study the effects of foreign direct investment (FDI) in growth of gross domestic product (GDP). It's considered that FDI positively impacts GDP growth and it makes sense to assume that ...
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23 views

Stationarity Testing on different time series data

For linear regression modeling, I have macroeconomic data that goes from 1985-2016 which i will use as my independent variable. My dependent variable data ranges from 2002-2016. My question is for ...
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1answer
21 views

Modelling in the presence of multiple cointegrating relationships

I am looking for some clarification regarding multivariable cointegration and what steps I should take to avoid spurious regressions. I am analysing a time series $y$ as a function of independent ...
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26 views

Smoothing time series

I have very basic experience with time series analysis and I am struggling to figure out the following. I have this graph which shows the counts of people in and out of a store for a day, the counts ...
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Estimate the production for 1995 and 2005 with the helps of following data: [closed]

Years Production(in Lakh ton) 1990 180 1995 ? 2000 250 2005 ? 2010 320 2015 400
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What are the prerequisites for time series analysis? [closed]

The question is self explanatory. What do I need to know/understand before being able to self study time series analysis? E.G. Probability theory (okay do I need the measure theory, sigma algebra ...
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36 views

Endogeneity issue in time series model

Can anybody tell me about endogeneity issue in time series? I've read one paper, discuss that income is likely to be endogenous for consumption. However, on the UK data, the current quarter ...
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Intuition behind the characteristic equation of an AR or MA process

Ok, so I've just started learning Time Series Analysis. We can write an MA(q) process as Yt = θ(L) ϵt and an AR(p) process as ϵt = φ(L) Yt in terms of the lag operator. Then, with no explanation ...
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48 views

Does using difference transformation lead to bias? (Levels vs differences regression)

Consider the model estimated in levels (also assume this is the true population model): $$y_t = x_t\beta + e_t$$ As usual we have the dependent variable $y$, independent $x$, the error term $e$, and ...
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Finding common patterns of activity of time - time series clustering?

I am interested in finding out whether patients attending medical services (e.g. Emergency department; GP surgery) do so in distinct patterns. For instance, some patients may attend at regular ...
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Classification on sequential data

Context: I am working on a classification project where I recommend items to customers based on their past purchase history. Question: How will "time leakage" affect training? Example: Let's say ...
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60 views

Correlation between two high frequency time series

I have two time series, both at a high-frequency level. My question has two parts: How do I calculate correlation in a high-frequency setting? I assume that the normal correlation theory would not ...
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Calculate covariance of slow and fast variables

Say you have two time series $X_t$ and $Y_t$ where $X_t$ is given by an $AR(1)$-process and $Y_t$ is a deterministic function of $X_t$: $$Y_t = f(X_t).$$ Also assume that the fluctuations of (the ...
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6 views

How to summarize MSE across groups?

I have a large amount of time series data collected for different groups over a 30 year period (dataset x) that can be broken down by sub-group. and corresponding time series data (dataset y) from a ...
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15 views

Econometric research female labour participation [closed]

I have decided to focus my econometric project on the determinants of the female labour participation rate. I have one dependent variable (the female labour participation rate), multiple independent ...
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Is every ARIMA(1,1,0) model equivalent to an AR(2) model?

Assume I have a time series $ x_t $ that I want to fit using an ARIMA(1,1,0) model of the form: $ \Delta x_t = \alpha \Delta x_{t-1} + w_t $ This could be rewritten as: $ x_t - x_{t-1} = ...
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1answer
24 views

Rolling Forecast Re-training Step Concept

I'm trying to understand the steps in Rob Hyndman's Multi-step forecasts without re-estimation example below. I'm wondering what the purpose is of ...
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questions about predicting time series using gaussian process regression

I am using gaussian process regression to predict a time series. The time series is number of daily active users(DAU) of an APP, and takes daily numbers of installing users and uninstalling users as ...
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Obtaining RSS from R strucchange package [migrated]

I'm trying to search for unknown structural breaks in co-integrated time series. $$Y_t = c_t + \beta X_t + e_t$$ Searching for breaks in this context is essentially the same as it would if both ...
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1answer
12 views

Interpretation of external regression coefficient in linear regression with ARMA errors

I am fitting a linear regression model $y_t = b\times x_t + u_t$ with ARMA errors $u_t$. Is the interpretation of $b$ the same as in usual linear regression?
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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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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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1answer
30 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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1answer
27 views

auto.arima Not Minimizing AIC

I simulated a MA(3) process using: ...
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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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1answer
43 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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22 views

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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45 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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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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1answer
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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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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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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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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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1answer
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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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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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1answer
28 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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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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29 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 ...