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Questions tagged [time-series]

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

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Is this a valid work-around for collinearity?

A fellow PhD student has monthly data on temperatures (T) and precipitation levels (P) for a certain agricultural region. He would like to use it to predict total farm revenues (Y) for year t: $Y_t=\...
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How to show fixed-effects coefficients in Stata?

I am estimating a fixed-effects model on a panel-data using the xtreg, fe command. I hope to show fixed-effects coefficients in the table as well. Can anyone tell ...
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Where does the white noise come from in MA(q) model?

I'm having trouble understanding the intuition of the moving average model. How does summing up a bunch of white noises related to predicting your particular time series data? Suppose I have a MA(q) ...
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Quality metric of sampled time series data

I have a time series that has too many points. I sample one in every 100 points, in order to reduce the amount of data I need to transmit from my measurement device. What accuracy metric can I use ...
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Can we use bootstrap in time series case?

I use random forest for time series forecasting.I have some features: lags. day of year,day of week,hours,minutes. ...
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How to get consistency in neural network and eliminate possibility of NaN values?

I'm using a neural network(Keras,LSTM) for time series regression. Whenever I run the network, I get different outputs for the prediction. This is presumably due to the randomised weight ...
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What causes the degeneration of correlation in a simulated time series?

I am trying to simulate an ARMA(1,1) process with the following characteristics: $\phi$ = 0.97 $\theta$ = 0.80 Standard deviation $s$ = 245 Mean $m$ = 1000 ...
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1answer
24 views

How to perform specific queries in weather data time series [on hold]

I have time series data from several weather stations located in a specific area. The readings include a timestamp, the humidity and the temperature. The resolution of the data is quite high, about 6 ...
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Time series: group and then forecast, or forecast and then group

Let's say we want to forecast revenue by month for the next 12 months, and we have daily revenue data for the last 3 years. We could then group this data by month, train our model using revenue by ...
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1answer
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Clarifying lag number selection in AR,VAR, VECM etc. models

When it comes to optimum lag length selection, we are supposed to comply with certain information criteria such as Akaike, Schwarz etc. As far as I know, either of them suggest the proper lag number ...
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Factorised form of Autoregressive Polynomial

I'm new to Time Series Analysis. I've read that when inverting autoregressive characteristic polynomial of arbitrary finite degree, we need to write it in its factorized form: $$\phi_p(x) = \prod_{i=1}...
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What is the next step after finding outliers using tso function in R?

I am trying to predict monthly sales for 3 years. After converting my data to timeseries object I ran tso() function to detect outlier. What do I do after this? The output has original series and ...
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Interpretation of fitted model with 'auto.arima'

I'm a little bit confused about the fitted results given by auto.arima when I was trying to fit a regression model with ARIMA error. The example is ...
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1answer
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Stationarize count based time series data

I have a count based time series sequence with lot of 0s. Usually to achieve stationarity we can do the following transform: ...
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Time series forecasting does not work on unseen/extreme values during validation

I have a conceptual question and not sure what would be the appropriate solution. I have run a time series forecast using arima methodology. I had several years of data that I used and split my data ...
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Decomposition Difference Between Twitter and STL Method

I am having a lot of trouble understanding the difference between the two decomposition methods: twitter and stl. https://www.rdocumentation.org/packages/anomalize/versions/0.1.1/topics/...
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Stationary processes that do not satisfy Gordin's central limit theorem

We are doing an assignment for our Advanced Econometrics course for which we are trying to illustrate Gordin's Central Limit Theorem by simulation. We used an AR(1) process to show that if the ...
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Statistical technique for multipoint time series data

Apologies, if the question is very broad, my knowledge of statistics is from social science background. Recently was provided data in form of ID 2014-01 .... 2014-12 , 2015-01...........2017-12 No. ...
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1answer
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Does R's arima() fit / use multiplicative or additive seasonality?

I have searched Cross Validated and read the documentation of the stats package in R, but I cannot figure out, whether the arima() implementation uses additive or ...
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What is the relationship between the prediction interval of an ARIMA(p,d,q) and the prediction interval of the original variable

The title may be enough, I want to know what is the relationship between the prediction interval of an ARIMA(p,d,q) and the prediction interval of the original variable. Lets say that d = 1, so that I ...
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1answer
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R - lme4 - Help on repeat measures over time, continuous predictor interactions

I've created an exmaple data set below to hopefully help answer a question in regards to a lme4 in R and measurements over time. The data is 4 plots, measured over 4 years, with the dependent ...
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1answer
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Time series analysis via generalized additive models: model assumptions and stationarity

I have settled on building a generalized additive mixed model using mgcv::gamm, on data and for purposes I have described in more detail here. In a nutshell, I want ...
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1answer
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Error in the standard deviation when simulating an ARMA(1,1) using arima.sim

I'm trying to simulate an ARMA(1,1) process whose autoregressive and moving average parameters are, respectively, 0.74 and 0.47. Moreover, I want the simulated data to have mean equal 900 and standard ...
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Maximizing the minimum R-squared over a period of time [on hold]

I'm currently running a regression of changes in prices against changes in yield and taking the coefficient as an estimate of duration. I use the past X days of price and yield changes (pulled from ...
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Using GLMM to explain vegetation change as a function of change in soil parameters

I have a dataset that consists of vegetation datasets (species/abundancy tables) and soil parameters (tested for ten parameters per soil core). Three different rounds of these data are recorded in ...
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Guidance on time-series change point detection or identification of contributions

Let me preface this by saying that I am not a data scientist. Please excuse any imprecision in my use of subject specific terms or notations. Please feel free to edit my question, to improve any ...
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In OLS, is autocorrelation/serial correlation still an issue when both regressor and regressand are time series data?

Suppose I am trying to figure out the slope between Jet Fuel and Brent Oil Index to hedge for price movement in Crude Oil, and say I have the following data available: Monthly Ending Price such as ...
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model selection for peak findings

I need to create a model, that finds when there will be a significant chance of occurring High peak in the signal. My data has N length of inputs and corresponding N length of outputs. For example, ...
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An alternative to seasonal analysis in ARIMA models (SARIMA)?

I recently became aware of a property of time series with which I was previously unfamiliar: that a finite-dimensional autocovariance function is equivalent to an infinite-dimensional power spectrum, ...
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Using a bi- variate Vector Auto-regressive model on 2 assets

I have two assets in a time series with serial auto correlation. I want to find the relationship between them. That is, if Y1 goes to up 10% Y2 should move up 5%. I would also like to know, if Y1 is ...
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1answer
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How to show random sampling implies no serial correlation in errors, so OLS assumption with no serial correlation is fulfilled

I am trying to prove the given random sampling, the $Cov(u_{i}, u_{j}) = 0$. Here is my prove: Assume given $y_{i}, y_{j}$ with random sample, where $y_{i} = \alpha + \beta x_{i} + u_{i}$. Also ...
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Poor error control in hierarchical linear models with lagged, within-person-centered independent variables

I'm interested in assessing the performance of a multi-level model (aka hierarchical linear model, aka linear mixed effects model) when examining time-lagged associations. My interest is in making a ...
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Time Series Prediction using multiple variables

My Dataset looks like Question: How can i predict 2018 weeks 13 to 18 using data of 2018 weeks 1 to 12 for Max temp, Min temp, Rain, Humidity, Wind Speed. Should I predict first Min temp, Rain, Humi,...
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Determining if two time series are cointegrated

I'm currently working on a problem where I am given that $\epsilon_t$ is a series of independent draws from a N(0,1) distribution $w_t$ is another series of independent draws from a N(0,1) ...
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1answer
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Understanding Intuition for ETS Damping Selection via AIC/BIC

I'm trying to understand how ETS selects whether to use a damped model via information criteria (I'm not sure which of AIC, AICc or BIC are used). I have a time series and I'm comparing two ETS ...
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Is diagnostic test essential in time series analysis?

Upon I read the papers discuss or propose time series models, I found that some of the papers exclude diagnostic test in their results of model fitting. Is diagnostic test such as portmanteau test ...
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1answer
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Timeseries with multiplicative noise in Stan

Say we have a monthly time series $y_t \geq 0$ dominated by seasonality, where the absolute differences from year to year are much smaller during low season. To avoid negative values and capture the ...
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Post-hoc test on linear mixed effects analysis comparing two treatments over time

I am trying to analyze my data in R and I would like to ask, if what I have done so far makes some sense, and how I could proceed. Let me try to explain my data first: I have two different treatments ...
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Is there any way to find the optimal point of a trade-off?

I have two series: S1: resources a company spent per number of employees (e.g., N=1->R=10, N=2->R=23, N=3->R=43, etc.) S2: profit the company makes per number of employees (e.g., N=1->P=100, N=2->P=...
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Performance comparison: ARMA VS AR [closed]

Background In my experience, ARMA model usually endup with similar performance as AR model. So in my experience, it's like there is almost no difference in using AR model or ARMA model. I guess ...
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LSTM : multi-step multidimensional multivariate multi-site timeseries forecasting [closed]

I'm working on a project in which i'm trying to do a pollution forecasting. I googled around and found that LSTM is a good candidate for this task, however, I'm still struggling at how to adapt it to ...
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How to test whether my count time series data is zero inflated and overdispersed?

There is the data set "weekly number of syphilis cases in the United States from 2007 to 2010 in the Mid-Atlantic states given in the ZIM package. How can I test wherever the data is zero inflated and ...
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1answer
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Forecasting algorithms for incomplete time series data [duplicate]

I want to forecast the demand of each SKU in my warehouse every week from the history transaction that I have collected. The data contains brand, product type, SKU, quantity, date(per day), price. But ...
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ARIMA Model- do i need to include exogenous variables?

How do you tell if exogenous variables should be included or if its ok to just build a model without them (the data speaks for itself).
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predict continuous variable over time for each row

I have many items with a start date, which produce continuous outputs over time for a few months after the start date. These outputs often follow a decay function but not always. So it is a bit like a ...
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Can someone help me to interpret these autocorrelation plots?

I've got these two acf plots that were produced in R. The first plot is the acf of a differenced time series (in this case tweets from twitter). The second plot is the acf of the remainder component ...
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1answer
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Explaining tourist numbers over time to historic sites, based on a set of predictors

I've been struggling for some time trying to figure out the most appropriate way to analyse some data. My task is to (hopefully) explain what may be driving the flow of visitors/tourists to two ...
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1answer
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Ensemble learning timeseries: Standard K-fold cross-validation ok for final step?

I have used 5 different classification models to predict future price direction (up or down) using caret's timeslice for each model type. I now want to put all the models predicted probabilities ...
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Pooled autocorrelation and AR model coefficients in a multivariate time series

I'm reading a paper where the authors generate AR(p) data for use in a sensitivity analysis. They get the coefs for the model by estimating the "pooled AR coefficients from the multivariate time ...
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1answer
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Is standard Brownian motion (AKA a Wiener process) weakly or strictly stationary?

Question Let $B(t)$ be a standard Brownian motion (AKA a Wiener process). Is $B(t)$ weakly or strictly stationary, particularly as defined here? My Thoughts We know, by definition, that its ...