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

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How can I do a rolling estimates of the median unbiased estimator of an AR model in R?

Given an AR(4) process, this can be rewritten in an ADF form as In order to estimate ρ, that in this case represent the sum of the autoregressive parameter, there is a procedure proposed by Andrews ...
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ARIMA seasonal part (0,0,1): makes sense?

Does it make sense to have a model with a seasonal part with zero differencing but a seasonal AR or MA component? How do I read this? No differencing means there is no a seasonality but then ...
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25 views

Handling missing values in function `prewhiten` in R [on hold]

I am using function prewhiten from "TSA" package in R. I get an error about NA values, but I don't understand it, because I ...
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1answer
23 views

Can negative relationship between X and Y be spurious

Let's assume I have X and Y and both X and Y have positive relationship. In such case in which both series trend in the same direction, we need to test for cointegration to be sure that relationship ...
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Print significant auto-correlation values in R [on hold]

If I do an autocorrelation test in R (using function acf), I get a great graph, and the horizontal lines show the cutoff of significance. Function ...
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15 views

Estimating accurately the mean of an autocorrelated bounded integer time series

I have a bounded integer time series $X_{1:\infty}$ ($1\leq X_k\leq M$), and I want to estimate the mean $$ s = \lim_{L\to\infty} \frac{1}{L}\sum_{k=1}^L X_k. $$ I'm assuming it exists and that $X_k$ ...
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LSSVM Prediction using LS-SVMLab toolbox v1.8

I'm trying to forecast a time series of air passengers using LSSVM with the help of the LS-SVMLab toolbox v1.8 from http://www.esat.kuleuven.be/sista/lssvmlab/, specifically the NARX model function. ...
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23 views

Statistics of performance

I have done some testing on the performance of a method. Basically, there are 4 materials I can use. Each will give me a different amount of results in a different time frame. I used these four ...
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19 views

Similarities between Pade approximations and ARMA(p,q) in time series [on hold]

I am wondering if someone would mind explaining how Pade approximations in calculus and ARMA(p,q) in time series are similar.
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16 views

Modelling Reflexivity with a Time Series Model

I'm new to this forum so hi to everyone! I would like to find a time series model that is capable of modelling two time series with following properties: 1) consider time series X and Y. X is ...
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1answer
19 views

Time series regression coefficient interpretation with differenced independent variable

I'm working on a project on time series regression. The independent variable was non stationary so I took first differences to stationarize it. Now when I regress it against the dependent variable the ...
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15 views

time-series analysis / forecast compared to real planning (controlling) departments?

The following case study: Planning and forecasting in a volatile setting by Amy Wheeler, Nina Weitkamp, Patrick Berlekamp, Johannes Brauer, Andreas Faatz and Hans-Ulrich Holst, published in Rob ...
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28 views

how to show that a decrease is significant in a time series data

I have 2 time series - one from the 1990s and one from 2000s. (The data is each decade is not continuous - meaning in the 1990s, I have data from 1991, 1993, 1996, 1997 and for 2000s, only from 2002 ...
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9 views

determine confidence bounds for turning points with RAPS

I'm computing the Rescaled Adjusted Partial Sums (RAPS) of a time series to identify periods of change in the data. A description of this method can be found in: Garbrecht, J. and Fernandez, G.P. ...
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33 views

Vector autoregression: many variables (10), short sample (100)

Suppose there are ten observation sites along the road. A, B, C, D, E, F, G, H, I, J. We obtain data at each site once in a day, in this order. That is, first go to the site A at 9:00a.m., and when ...
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2answers
26 views

Dynamical time warping for dynamical system classification

I am trying to find a way to classify and segment a large set of time series that each individually describe a dynamical system. I wanted to know if the following idea for doing so is a feasible ...
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18 views

X-13 Seasonal Adjustment trading day peak in regarima residual

I have a time series of air passengers with a total of 143 monthly observations. Having done the seasonal adjustment using X-13-ARIMA-SEATS, the final diagnostics seem to suggest that the seasonal ...
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18 views

Measuring promotion baseline smoothing

I am trying to estimate promo effectiveness for a retail product. To do this I have taken monthly quantity sold data for the product. My question is - Is it logical to model the sales baseline (sales ...
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10 views

Jittery sampling of jittery events - what to prioritize

Stat beginner with a practical problem. In an industrial application, I'm sampling a repetitive production process, to generate a histogram of the cycle time. The cycle time of course varies somewhat ...
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17 views

Time series pattern match in large datasets

I want to find in a time series pattern that look similar to a predifined one. At the moment I am using DTW to do this and it works well. But now I want to use it for larger datasets like finding a ...
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25 views

A time series which cannot be made stationary

I have a series of data points, of days between successive earthquakes. I am trying to model time between earthquakes, in order to predict waiting times. Using Minitab I have tried differencing, ...
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1answer
29 views

VAR/VECM/ARDL optimal lag selection

Question 1: Is it necessary to consider AIC and the BIC criteria when selecting the lag for a VAR, VECM or ARDL model OR can I use something else? Example: Can I pick 12 lags because the model simply ...
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References for Combine Seasonal Period in Seasonal Time Series

I'm in a middle writing my thesis. I got confused for find the references which said period of seasonal could be chosen from the small period which already containing another periods. Here is my ...
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73 views

Time series forecasting with many predictors

Suppose I want to forecast a time series $y$ using its own lags and a large set of potential candidate predictors $X$. The model could be specified as follows: $$y_t = a + \rho \cdot y_{t-1} + \beta ...
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Google Trends: Stitching 90 day periods of daily data together

Google Trends lets you see the amount of researches made on for a term on google during a set period of time, normalized between 0 and 100 (depending on the highest value in that period). I would like ...
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Rules of integrated series and balanced regressions

Background There are various rules of linear combinations of integrated series. Let's just consider the $I(0)$ and $I(1)$ cases. For example, if $x_{t} \sim I(1)$, $y_{t} \sim I(0)$, then $ax_{t} + ...
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PCA to decorrelate and classify timeseries

I have a labeled dataset where each subject belongs to one of two classes A or B, with A ...
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17 views

Interpretation of level, trend and seasonal indices in Holt-Winters exponential smoothing

I am trying to learn Holt-Winters exponential smoothing. In the algorithm there are three indices involved (level, trend, ...
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1answer
22 views

Algorithm to determine a point in time series data, after which probability of increase in value is very low

I am working with dataset which contains number of movie tickets sold per day. This is basically a count of total number of tickets sold, for a particular movie, for each day after its release date. I ...
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41 views

Predicting time series using `arima` or `fitlm` in Matlab?

I have 6 sequences (time series); they all belong to the same variable. I divide each sequence in two parts having 80% and leaving the last 20% for validation. I am doing the analysis and modelling in ...
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10 views

R: How to pack the seasonal components of time-series to an array-like structure? [on hold]

I am trying to calculate the decomposition of 34 time-series to study the similarity of seasonal components, as explains this this tutorial I start from a CSV that, after some transformations and ...
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26 views

Testing for white noise for non stationary time series

I could not find a question where testing for white noise for non strick stationary non parametric time series is adressed. Per definition white noise is stationary. But finding a series that is ...
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24 views

Modelling effect of advertisement on sales with ARMAX

I am trying to model the effect of advertisement on sales in Stata. The data is weekly and there are around 150 observations. I started by applying an ARMAX(1,0,1) model with the following exogenous ...
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16 views

Suggestions for Neural Network Structure for Time-Series prediction with constant covariates

I've been working on a time series prediction problem and wondered if someone has run across a similar problem structure & can make a suggestion on how to structure the training data, network, or ...
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Time series and images : difference and terminology

A time series is an ordered collection of random variables. Considering a one-dimensional time series $A_i = {a_{i1},a_{i2},\ldots,a_{it}}$ where $t$ denotes the time index. So, the time series is a ...
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11 views

Residual Not Normal for Model Seasonal Time Series in R

I got a problem when choose the model for forecasting with time series. I'm in a middle writing my Thesis. My data have a seasonal pattern so, i tried use this model ...
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18 views

Sum of the AR coefficients and First Order Autocorrelation Coefficient

I'm working with quarterly inflation, usually a AR(4) and I want to obtain different measures of persistence, that are: 1. the sum of the AR coefficients Σα 2. First Order Correlation Coefficient, ...
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43 views

Estimating unconditional variance in time series

Consider a time series process with a well-defined, finite unconditional variance. Given a realization of the process (a time series) and a model for it, there are at least two ways of estimating the ...
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14 views

Combining continuous spatial and discrete time series methods for spatial prediction

Here's something I've been pondering. Wondering if anyone can shed come light on it/recommend some references/tell me why it makes no sense, please. In my field (predicting crime risk by location), ...
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16 views

Estimate battery's real capacity in real time

The maximum capacity of a battery during usage can vary from manufacturer's specifications due to factors like temperatures and battery health. Because of that i want to estimate the real maximum ...
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18 views

How to interpret the result of tbats{forecast} in R?

If the values of alpha, beta and gamma are not Null it implies that the data has trend and seasonality. What does the negative value of beta or gamma implies? ex - gamma is: -0.0000038297240918093.
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R : Time Series forecasting with TBATS [closed]

I have a time series dataset with daily values from 2013-10-01 to 2016-01-30. There is a clear monthly seasonality with months October, November recording higher traffic.I tried forecasting using ...
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55 views

Can we identify ARIMA model without looking at ACF and PACF plot?

Can we identify ARIMA($p,d,q$) model without looking at the ACF and PACF plots? I am trying to write a generalized R programme for fitting time series models. We may find out the orders $p$, $d$ and ...
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Data setup: Attrition/Churn Modeling with Time Dependencies

Beginner Data Scientist here... I'm setting out to build a predictive model for our client in the hotel/hospitality industry to explain the factors contributing to the attrition of their Loyalty ...
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54 views

Sample size for best forecasting ARIMA model

How can we decide the size or portion of the data given to get the ARIMA that has the best forecasting properties? I mean, for example, we have a hourly series with over 28.000 elements. Which is ...
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18 views

Fitting a model with time of day, day of week adjustment

I have one year's worth of clickstream data (like Minute, Numvisitors). I'm trying to see whether external events (commercials) affect ...
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Time evolving non parametric data model

I have data points $(x_i, y_i)$ in each iteration of my algorithm on which I build a non-parametric model using local linear regression with a Gaussian kernel. Using this model, I estimate the ...
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Limiting the number of autoregressive terms in ARIMA in R [closed]

I am using the below mentioned R code to implement a simple ARIMA process in R: forecast<-arima(log_visits_ts, order=c(3,0,1),xreg=reg,method="CSS") Question: ...
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39 views

Constrained regression and lagged factors

I have a model that I need to estimate, where I've seen a similar example (Constrained Regression in R: coefficients positive, sum to 1 and non-zero intercept) but without the second part of ...