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

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How to plot the forecasted values against actual values observed later in R? [migrated]

We used the R library forecast to make predictions for the next 24 hours. We have the following: ...
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Forecasting with mixed frequency data

Just a general question that I couldn't find too much on. What would be some good approaches to one step ahead forecasting of financial time series with mixed frequencies? Often a lot of the ...
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33 views

Why unit root test with D-F not using normal or t test?

Let $X_1, \dots , X_n$ be observations from the AR(1) model. For large $n$, the maximum likelihood estimator $M(\phi_1)$ of $\phi_1$ is approximately normally distributed as $N(\phi_1, (1−\phi_2)/n)$...
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What is the approporiate test?

I have two variables ; First variable is ordinal and it represent years series from 2006 to 2015 and another variable which is interview score and I want to study if there a relationship between the ...
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Would including “year” as a categorical random effect remove a long-term trend in a mixed effects model?

I am trying to detect evidence of warming in a monthly temperature time series over a 20-year period by testing for a trend. I have precisely followed the method of Crawley (2013) The R Book, 2nd ...
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What is the probability of the first positive event in an sequence of binary events where sequences have finite but random lengths?

I have a time series of observations from a longitudinal study of individual objects. These observations are seen as discrete sequences of features, one sequence per object. The sequences have ...
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Is this uncertainty representation correct?

I have a set of execution times for some processes. But there is uncertainty in these values. I call this $$ET + \lambda ET $$, where $\lambda ET$ is the delay. I sample $\lambda$ from a gamma ...
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28 views

FInding relevant features for a time series segmentation

I have a time series data, where each of the data point belongs to one of the known clusters. What I am interested is to perform a HMM so that we can obtain hidden states that further abstracts out ...
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hts in R: Convert data.frame into a gts object [closed]

I'm a month-old user of RStudio. Currently, I'm working with hierarchical time series analysis with the hts package. After reading my .csv data below, ...
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Time series with slightly unequal intervals

I'm very new to statistics, and I have a problem that may or may not exactly be considered a time series analysis problem. I have a large set of vehicle location measurements (x0, y0)...(xt, yt) taken ...
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Methods for time-series pattern weightings in machine learning

I am currently developing an anomaly detection algorithm to test for anomalies in system usage. This is obviously very seasonal in nature (for example, during the Super Bowl, for example, there will ...
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Across time correlation

I got two data sets (different omics data) from several observations across time (Two data sets will look like N*P1*T, N*P2*T, N=number of observations, P1/2=number of variables of first/second omics ...
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Adding together ARIMA forecasts vs an aggregated model?

Say I am forecasting sales for a company that has four regions using ARIMA models. Each region behaves a little differently so four different ARIMA models are used. In order to forecast overall ...
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What's the best way to compute the offset of a time series?

Say that I have a signal $f(t)$ composed of an offset, another signal and noise: $$ f(t) = offset + g(t) + \epsilon(t) $$ The function $g(t)$ is strictly possitive but the epsilon can make the ...
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154 views

Appropriate time series model with multiple observations at each time point?

Data description: My dataset (~2 millon rows) includes cattle price records from weekly cattle markets during the period Jan 2008-Dec 2014. For each animal, the following information before the sell ...
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53 views

Sliding window and historical data

In my problem I have a longer period of historical data of a time series. I need to predict for some specific points in time in the future. For these points in time five previous values are also ...
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Difference between first one-step ahead forecast and first forecast from fitted model

I'm doing some time series modeling using R and the forecast package, and found a minor difference I couldn't figure out. I'll reproduce my steps below. First, I ...
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How to analyze differences between two time series collected under two different conditions

How should one analyze time series data that were collected during two seasons: season 1 and then at season 2, where during each season a measurement sample for several continuous variables were ...
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Making a time series prediction for events per second based on past data

I have data values for events per second (EPS) present in log files pertaining to various devices. The idea is that these values should help us observe a trend and create thresholds for specific times ...
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How are errors terms calculated in GARCH model by rugarch package?

I am fitting a GARCH(1,1) model to the data and want to look at the innovation distribution. ...
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33 views

Book about time series analysis in Stata

Does somebody know a good book which outlines the time series analysis in Stata, that is, the various commands explained. I am aware of the Stata manuals; however, they are not that user friendly for ...
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How to optimise an automatic ARIMA-model selection?

I've been using statsmodels.tsa.arima_model to fit the residual component of some data. I've written an algorithm to automatically select the ARIMA model. Results ...
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(Cross) Correlation of time series with very different sampling intervals (sec. vs days)

This is my first post on Cross-Validated. I read a lot of question related to my problem, but no one was completely satisfying. I have two time series that are sampled at very different time ...
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What's the variance of the following stochastic integral and is it weakly stationary?

The stochastic integral is defined as $$u_t = \int_{t-1}^t e^{-\kappa(t-s)}\int_0^s e^{-c(s-r)} \, dW(r) \, ds.$$ where $W(t)$ is a standard Brownian motion, $\kappa$ and $c$ are both positive. I ...
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Pacf lag axe , is not an integer

I'm trying to use ARIMA process to predict the behaviour of a time series, the probleme I face is that I can't get the order of each component of ARIMA, the lag is between 0 and 1, same goes for the ...
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Run-like pattern in candle chart

To my untrained eye a pattern appears in this candle chart, where down-days (dark purple) tend to occur consecutively. I have a very basic understanding of statistics and R software, but it's been a ...
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34 views

Best approach for selecting averaging weights

I am trying to build an R tool for forecasting a (hopefully) wide range of time-series. I have settled on using several models, taking the forecasts from each, and deriving a weighed average of them ...
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Is there a way to prevent forecasting negative values with ARIMA (or add constrains) in R?

Currently I'm using the ARIMA provided in R, the training series is a seasonal time series, with some values close to zero in each period, and I find that when the training series have a descending ...
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31 views

Correlation between vectors with non-matching y values without using interpolation

Is there a way to calculate the correlation between two time series that have been adaptively downsampled and thus (may) have different y values? This is easiest to explain with an example, so ...
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Causality and stationarity of AR models

Studying AR models, I found that there are two properties that these models can have stationarity and causality. For what concerns stationarity, I have studied that this condition is satisfied if ...
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Flow Network Problem

Imagine there is a city with a network of roads. There are specific number of entry/ exit points in and out of the city as well as interior roads that connect points within the city. There are a ...
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time stamp as input variable for regression (feature extraction)

I am working on web logs and have a time-stamp variable in the format dd-mm-yyyy hh-mm-ss. I have earlier worked on date variable and found that best way to extract feature from date is to create ...
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35 views

Time series and instrumental variables

I have a time series model that suffers from endogeneity. In other contexts it would be reasonable to use instrumental variables. However, I have not seen this done before with time series. Can I ask ...
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What test in R check whether the variance of the error term is constant or not for time series model?

I saw people test it by plotting the residual term and its histogram. I wonder if there is a method in R that does the test. Thank you.
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Marginalized Denoising Autoencoder for Regression/Prediction

I have been looking online at papers etc. about marginalized denoising autoencoders (mda) and everything I've found so far uses mda for pre-training layers for a classifier such as a support vector ...
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Subset of a time series, bad value for start in windows function [migrated]

this is my first post and I hope you can help. I am trying to extract certain period from a time series. My time series (tsSST) goes from 2011,1,1 to 2015,12,31 and I need to extract the period from ...
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Neural Network for Forecasting Time series

I have a dataset of monthly sales for the past 6 years. Significant attributes in the data set are: Region, Nameplate, Segment MonthofSale and TotalSales. I ...
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What is the formulaic relationship between R2 and correlation coefficients between different units in a balanced panel data set?

I am trying to understand the relationship between R2 and correlation coefficients between different units in the underlying data. Specifically, I have a balanced panel data set, with N different ...
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Bayesian parameter estimation

Here is an exercise from a past exam of my course in Time Series Analysis Suppose you have observations $(Y_{i,t}; x_{i,t})$ on $n$ units $i = 1, ..., n$ at time $t$, with $t = 1, 2, ...$. ...
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How to calculate percental change of two time series?

I have the two sets of data; $A = $ 1.5010, 1.3118, 1.4219, 1.1650, 1.2720, 1.5421, 1.4211, 1.1832, 1.5378 1.4357, 1.2707, 1.2411, 1.4833, 1.5578 and $B = $ 1.4039, 1.1912, 1.3596, 1.4168, 1....
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what is better for demand forecasting, NN or ARIMA?

I want to predict future demand based on data I have. I hava already used ARIMA model but it is not giving very high accuracy. So should I also try NN or ARIMA is better?
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How to compare multiple time series with a single time series and quantify upward/downward trend relative to the single time series?

I have been reading about time series comparison and haven't really found an answer to my question. I have around 1000 stores which I have clustered based on trend over time and identified 8 groups of ...
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42 views

Fourier terms to model seasonality in ARIMA models

I would like to use Fourier terms to model seasonality in an ARIMA model. The reason for using Fourier terms instead of a seasonal ARIMA model is that the frequency of the time series is very high (...
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dependent variable in an error correction model

I am going to examine the impact of interest rate, inflation, housing expenditures on the ratio of daily consumption to income. I would like to use Error Correction Model. The unit root test shows ...
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Population models - statistical analysis over time of subpopulation expansion

I am struggling with a statistical method to assess whether two populations have "changed" over time. In my case I have a clonal bacteria species, some strains with a mutation conferring resistance ...
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22 views

Forecasting a cumulative variable in time series

I'm trying to build a time series model based on a cumulative variable that never decreases. I'm interested in knowing when the observable will reach a certain value (i.e., when it will intersect ...
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Seasonal ARIMA for each weekday instead of double seasonal ARIMA

I have read some papers on forecasting time series with double seasonality (e. g. hourly data with daily and weekly seasonality). I understand that double seasonal ARIMA can be used for that purpose. ...
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Running Linear Regression Model

my question is can i run linear regression model using summarized counts/frequencies? For example, my dependent variable is total number of people who is aware of a specific TV show and my independent ...
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Detect increase/decrease events on time series

Given a time series, I have to detect two types of events: 1) "medium" decrease 2) "high" increase Event detection should be "fast enough". I used quotation marks as I'd like to set different ...
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Normalizing year data

I have a data set in which there are data from April 2010 to Dec 2010 ( 9 Months) Jan 2011 to Dec 2011 (12 Months ) Jan 2012 to Dec 2012 (12 Months) Jan 2013 to Dec 2013 (12 Months) Jan 2014 to ...