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

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120 views

Under what circumstances is an MA process or AR process appropriate?

I have a very basic question. Please let me know if this has been asked before, but in my defence I haven't seen it on Cross Validated. I understand that if a process depends on previous values of ...
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1answer
51 views

How to forecast time-series bounded by [0,1], i.e., forecast relative frequencies?

I am working with time series values which are all in the closed interval [0, 1]; these values represent relative frequencies, i.e., empirical probabilities. I would like to create a model such that ...
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1answer
28 views

Combinef in R HTS package- constrain to keep forecasts positive?

When using the combinef function from Rob Hyndman's very useful hts package for forecasting hierarchical and grouped time series, there does not seem to be a way to constrain the optimally combined ...
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0answers
16 views

Specifying a glmm for panel data

I'm trying to predict counts based on variables sampled on a monthly basis as well as a few that are not related to time. In several places I've read that the MCMCglmm package in R would be ...
0
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1answer
31 views

Multivariate Time Series

I am trying to learn multivariate time series using R. I have two time series and I want to see if I could use one of those to predict the other one, and after that check if the model holds or there ...
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1answer
31 views

Simulating non-causal time series?

I'm looking into the possibility of using a non-causal time series filter for some data. The goal is filtering (for the purpose of anomaly detection). However, this is not particularly relevant. I'm ...
2
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1answer
22 views

Separating and identifying long and short term effects of statistical variables

A quick general question: In a practical setting, what's a good way to separate out, and then comment on, long term and short effects in a model? I had thought a good way to do so would be to include ...
2
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4answers
142 views

Can a trend stationary series be modeled with ARIMA?

I have a question / confusion about stationary series required for modeling with ARIMA(X). I am thinking of this more in terms of inference (effect of an intervention), but would like to know if ...
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2answers
55 views

Interpolating time series

what are best ways to interpolate time series? I have three data points(1980, 1990 and 2001) and I need to interpolate them. Using R na.approx doesn't seem to be what I need since the data I need to ...
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2answers
41 views

cointegration - same thing as stationary residuals?

So I'm aware that cointegration means there is some linear combination of the set of variables that is stationary. So, if you do a regression and find stationary residuals, can you just immediately ...
2
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2answers
65 views

Avoid negative results in Holt Winters forecasting

I understood that Holt Winters forecasting may results in negative values due to trending. I did reduce trending component value, but still forecast values are negative territory. Our data set will ...
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0answers
34 views

Turning general regression into time-series prediction

Suppose you have a general regression model, which behaves like a black-box to you. All you have is a $\ \ \text{train}(X,Y) \ \ $ function, where you input your predictor matrix $X \in \mathbb ...
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13 views

What approach to use for attrition analysis?

I am trying to perform an attrition analysis on a company with an average size fo around 250 to 300. I have monthly attrition data for the last 30 months or so. Now if i want to go for a predictive ...
3
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2answers
56 views

What are some methods for generating simulated time series data for use in modeling?

I have a data set which consists of 50 observed years for which I have date and inflow values between a river and a reservoir. The data is formatted as follows: ...
0
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2answers
70 views

Best way to test for co-occurrence of measures

I have some data with temporal measures over time. I'd like to test whether two binary variables co-occur more often than chance would predict, and I'm wondering the best (simple) way to do that. The ...
2
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0answers
8 views

Methods for measuring snowball effects in a “complete” longitudinal dataset

I'm looking for ways to test for "cumulative advantage" effects in a longitudinal dataset (see image) I guess the data set is principally similar to this: http://www.caldercenter.org/whatis.cfm , ...
2
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3answers
62 views

Gaps in time series and time series validity

After doing some reading on CrossValidated, I understood that we can use "imputation" techniques to fill in the gaps (if they are random). But I am not clear on following questions: How many ...
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0answers
8 views

custom axis labels plotting a forecast in R [migrated]

I'm trying to get some sensible labels on a forecast. Here's my code: ...
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1answer
20 views

representative baseline for time series-like data

I have data for several individuals that takes the following form over time. Many individuals are flat over the course of a year, meaning their measurements stay roughly the same. Others have a peak ...
3
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1answer
57 views

How do you create variables reflecting the lead and lag impact of holidays / calendar effects in a time-series analysis?

I am working on a time-series project in which I am forecasting the daily activity of something (let's call it 'Y') based on three years of historical data. I know that Y is affected by calendar ...
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0answers
9 views

Comparing two time series over different periods of time

I have two time series that I wish to compare their movements. The issue is that one time series is daily data since 1920 and the other is daily data since 2008. When plotting them separately over ...
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0answers
14 views

How to fit two or more datasets with different occurence for regression

I want to run a regression in R with different datasets. The question is whether stock performance (daily log return) is influenced by factors like interest rates (the one set by fed or ECB), size of ...
0
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1answer
26 views

Time series: Stationary R-squared vs R-squared (Forecasting in SPSS)

I would like to check how two time series are correlated - how one of them predicts the other one. I have data for 27 years. I conduct my analysis in SPSS (Forecasting command, ARIMA) and I get: ...
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20 views

linearity of a time series

I am currently trying to correct forecast data using Kalman filter (python). I do not know where to start. I wanted to know how can I do a test to Know if my time series is linear or non linear? Is ...
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0answers
16 views

Regression line fit for linearly increasing data with manual reset

I've a linearly increasing time series dataset of a sensor with value ranges between 50 to 150 on which I've implemented a simple linear regression algorithm to fit a regression line, and I'm ...
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2answers
51 views

R: How to to simulate ARIMA using starting values?

I have built an ARIMA(p,d,q) model, m using say, m <- Arima(ts.data, c(p,d,q)) Given some starting values, I want to simulate future values based on the ...
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1answer
81 views

How to dampen forecast to improve accuracy?

According to Armstrong there is ample empirical evidence that dampening trends in uncertain and complex long term forecasting helps improve accuracy/reduce forecasting errors. What I'm not able to ...
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2answers
47 views

How to analyze multiple variable time series - suggest references

I have multiple environmental time series variables (for example: temperature, dissolved oxygen, conductivity, depth) measured every few minutes for several months. The variables are measured at ...
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27 views

Reducing high dimensionality as well as feature selection on multivariate time series

Lately I've been reading a lot about time series clustering as I want to search for similar patterns in my own data set. Even though I feel like I understand the basic concepts of this task I still ...
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1answer
19 views

Confidence Interval Application to a time series

So I'm not sure if I 100% grasp confidence intervals. Say I have a huge data set of a bond prices from 1996 to present in MINUTES. Suppose I separate each data by day. If I were to use a Dickey Fuller ...
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0answers
19 views

Predicting the missing data out of three values in each of the two vectors

I have 2 vectors of rural and urban populations of the same country. (years from 1975 to 2020) with only three values (1980, 1990 and 2001 years) in each. And I need to predict the missing data. My ...
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1answer
28 views

Predicting on data consisting of many independent short time series?

I have a dataset which contains many (hundreds) of short (3-30 obs) timeseries of different lengths. Each series is currently represented as a number indicating how long it took to the next event. To ...
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0answers
17 views

Creating classification features from wavelet transformed time series

I'm interested in using a wavelet transform, Haar for example, to create classification variables from time series data to use in logistic regression. Simple example. Let's say I'm trying to predict ...
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2answers
182 views

Estimate ARMA coefficients through ACF and PACF inspection

I know that this is probably a question that's been asked plenty of times, but i haven't seen an answer that's both accurate and simple. How do you estimate the appropriate forecast model for a time ...
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0answers
21 views

Interpolation and forecasting out of 2 values [on hold]

I have a vector of yearly population data from 1980 to 2020 with only two values (years 2000 and 2010) and I need to predict the missing data. My first thought was to use na.approx to fill in the ...
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2answers
23 views

Can time be squared to develop a curvilinear model of crop yield against time?

I am developing a linear model of yield against time (33 years of yield data) where year is 1975,1976....2007. I want to know whether change in yield over time was linear or not. So I fitted a linear ...
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1answer
38 views

How can I detect spurious regressions results?

I run bivariate Granger-causality regressions. Let $y_{t}$ and $x_{t}$ be stationary time series. I test if $x_{t}$ can forecast $y_{t}$ with the following regression: $$y_{t+1} = \alpha + ...
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1answer
24 views

Measure accuracy of Holt-Winters model

I'm really confused about measuring the accuracy of Holt-Winters fitted models applying different transformations. How do i compare the accuracy between models when i apply no transformation to the ...
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1answer
73 views

What econometric model to forecast a seasonal commodity demand while incorporating exogenous Information?

I have a monthly commodity demand and try to forecast this series for the next 5 years. Here is a plot: Of course, the natural approach to forecast this seasonality would be some kind exponential ...
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45 views

What is the minimum number of data points required to use ARIMA(p,d,q)?

I'm using the $\mathrm{ARIMA}$$(p,d,q)$ model to predict future time series data in R (see also R's ARIMA documentation). Q: What is the minimum the number of data points we must have in the time ...
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1answer
94 views

Can First Differencing Cause Negative Serial Correlation

Ex. series, say stock prices 103 101 102 150 101 102 100 First differenced 2 1 48 -49 1 -2 Notice you could guess a very large negative number following the very large positive in the first ...
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48 views

Mixing two linear regression models

In time series analysis, I have one predictor $X_1$ that has a higher $R^2$ when regressed against $Y$ sampled at 10 minutes interval. Another predictor $X_2$ has a lower error when fitted against $Y$ ...
2
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1answer
48 views

Show Regression with Arima Errors Equivalent Form of Differenced Variables

How can you show that the regression $y_{t}=\beta_0 + \beta_1x_{t}+\eta_t$ where $\eta_t$ is arima(1,1,1) is equivalent to $y'_t = \beta_1x'_{t}+\eta'_t$ where $\eta'_t=\phi_1\eta'_{t-1}+e_t$ and $'$ ...
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1answer
64 views

Analysis of spatial data over time and space

I have a data set having year-wise monthly average of minimum and maximum temperatures of 32 stations around the country since 1948. The latitude and longitude of the stations are given as well. I ...
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0answers
12 views

Error running optim function with STAR from book example

I'm running an example of Smooth Transition AR (STAR) Model from the book "Analysis of financial time series, 3rd edition" by Tsay, in section 4.1.3. The script is as follows: ...
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0answers
39 views

Rolling Window Forecasts in R

I have a question: how do I use rolling window forecasts in R: I have 2 datasets: monthly data which I downloaded from Google. monthly data I downloaded from the CBS (central bureau of statistics ...
0
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1answer
10 views

Combine results with different veracity

I have 3 neural networks processing 3 different vectors of values. Each NN processes a sample of it's vector and gives binary result (y/n) that is correct with given probability. All 3 NNs give answer ...
2
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1answer
39 views

Which clustering technique to use for a temporal dataset?

I have seen a similar question but thought I'd ask my own to hopefully garner some usefull feedback. Basically, I have a large temporal dataset, consisting of domestic smart energy meter use ...
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1answer
38 views

Forecasting a time series with weights

I'd like to forecast (or predict) a time series with weights. The following works using the regular linear modelling techniques ...
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1answer
37 views

How to perform seasonal adjustment to a time series?

Assume following data set representing each month of the year 2013 with the corresponding consumption of natrual gas to heat my flat and the respective mean temperature. How can I seasonal ...