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

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Time series forecasting with R

I try to forecast my web visitors on the web site for 10 future days using time series. My time series is daily. I have used an auto.arima() model. Considering ...
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Training on time series data with a small number of examples

The data I have is collected from 16 smartphones - it's made up of discrete readings from various sensors (eg. accelerometer in 3 axes, intensity of sound in various frequencies etc.), at regular ...
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Walkthrough of building a time series model (on real examples)

I'm trying to find some real examples showing someone going through the full process of building a time series model (how they deal with trends and seasonalities, what features they picked, etc). Does ...
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Large Time series analysis

i have a simulation experiment with 2187 different treatments (composed of 7 factors with three levels each) and 100 repetitions per treatment. Each repetition is monitored during 9,000 simulation ...
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What is the best way to apply a dimension reduction to a time series, and not to be affected by the outliers?

I want to apply a dimension reduction to a time series, in order to not have a high dimensional one, but I don't want this transformation to be affected by the outliers my time series could have.
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Error in `auto.arima` in R: AIC value approximated

I am running an ARIMA model for my forecasts in R My data set is 1 month's data. It has 2976 observations which has a frequency of 15 min. I recieve data every 15 min. There is little seasonality in ...
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25 views

Trend Decomposition

I have a time series of daily advertising revenue. The series shows various trends that are noticeable at long-term scales, such as over the course of a few months, but also many smaller ...
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cumulative Impulse Response Functions in R, error?

I am trying to calculate impulse response functions using vars package of Bernhard Pfaff. I am getting somehow confusing results. Running the following code: ...
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prediction on short time series with seasonality and data correlations

I have, say, 5 weeks of data standing for daily income of a company and I want to predict the next income. Obviously, there is a seasonality in data - every day is "seasonal" with the same day of the ...
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Use of One Way ANOVA

In order to analyze the financial performance of Life Insurance Companies in India during a period of ten years, year wise certain financial ratios have been computed for all the selected companies. ...
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Cumulative vs. non-cumulative IRFs in R

I am using irf function from vars package. I am trying to derive cumulative IRFs. The following code describes the case of deriving cumulative IRFs: ...
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Why is Symbolic Aggregate Approximation in Adaptive Data category?

Following this question in CrossValidated, I don't understand why is SAX in adaptive category, once Piecewise Aggregate Approximation is in non-adaptive category. Also, I was looking for a more ...
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Confidence interval for maximum value in velocity time series

I have a time series of velocity values, which is structured into two periods. The first period describes the baseline velocity ("pre"). At the beginning of the second period ("post"), a stimulus is ...
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Doubt regarding statistical testing of predictions made by two different time series models.

What test (parametric or non parametric) do you suggest to test the significant difference between the predictions made by two different models used in forecasting a given time series?
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Simple Example of Autoregressive and Moving Average

I am really trying, but struggling, to understand how Autoregressive and Moving Average work. I am pretty terrible with algebra and looking at it doesn't really improve my understanding of something. ...
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3answers
41 views

Help w/ finding correlation between unemployment rate and home prices over the same time period?

I am interested in analyzing the correlation between nationwide home prices and nationwide unemployment rates, both of which are leading economic indicators. I have data on nationwide home prices by ...
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28 views

What statistical test should I use for my crime rate data?

I apologize if a similar question was asked, but my statistics knowledge is limited. I have crime rate data for ten US cities ranging over the past ten years. I am trying to prove that crime rates ...
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10-fold cross validation for forecasting time series with explanatory data ?

I saw that the question was asked some years ago here, but I wasn't satisfied with the answers so I'm asking it again. Is there some theoretical foundations about not doing k-fold cross validation ...
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Ljung Box test for residuals of constrained ARIMAX(2,1,0) model

I have this ARIMA(2,1,0) model with one exogenous variable: $$\Delta y_t=c+\phi_2 \Delta y_{t-2}+\beta_x x_t+\varepsilon_t$$ I want to run Ljung Box test of residual autocorrelation with test ...
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Ways to stabilize OLS betas [closed]

I am estimating the parameters of a system of OLS equations in Matlab. $y=X\beta+\epsilon \to \hat \beta=(X'X)^{-1}X'y$. My $X$ is a $5\times 5$ matrix and $y$ is a $5\times 1000$ matrix, so $\beta$ ...
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Fitted value in GARCH(1,1) is the same as original data

I am trying to remove the volatility of my sample time series by fitting the GARCH(1,1) model with Gaussian innovations.The time series I use is the log returns on the daily closing value of S&P ...
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Failure time events with escalating severity

I have participants (in a randomized clinical trial) who are not merely failing therapy by experiencing the clinical outcome, but are also escalating to a more severe clinical state. I'm looking for ...
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Optimal method for identifying correlation of a stock price and various moving average durations for Python

I am trying to find the optimal method in trying to find the relationship between daily stock price and rolling correlation of another stock. For example, let's say I have a hunch that there is a ...
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Which technique will tell whether mortality is seasonal or not?

I want to analyze monthly death counts with respect to age, cause and gender. The data I have is about the name, date of birth, date of death, gender and cause of death of about 2000 cases for the ...
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Common methods for transforming non-normal variable to close to normality

I have a list of time series which contain negative values. Right now I am transforming the time series to all positive values >0 and using the Box-Cox transformation to reduce non-normality. My ...
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2answers
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Combining Linear Regression and Time Series

I’m trying to figure out if I can combine linear regression and a time series model to help make predictions about the number of shots in a soccer game. When I perform the linear regression, I have ...
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ARIMA analysis disrupted by “frequency” and “period” terms

Data interval is day, i.e. daily data span over 5 years no data recorded on weekends and holidays a weekly pattern exists Representation in R zoo An easy way ...
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What is the best statistical technique to optimize Online Advertising Spend?

I am trying to optimize marketing spend across multiple websites i.e., Nanigans (Facebook), Google, etc, to increase customer conversion (purchasing). Each ad placement results in two things: new ...
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Estimating model's parameters from repeated measurement of a process, concept and application in R

I've asked a similar question here. A process is observed on various days, where each observation is a time series. for example the above figure shows 5 of these observations. My goal is to perform ...
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36 views

Interpreting VAR Granger Causality on Eviews

I am currently conducting a multivariate time series analysis on Eviews. I am investigating the causal relation among various economic variables. I have estimated a VAR model using the Toda-Yamamoto ...
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Casual impact implementation of the Kalman filter and Bayesian structured time series

A short question does the package casualimpact, for R, use the full Bayesian Structured time series methodology including the Kalman filter?
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Use ACF and PACF for irregular time series?

Given an irregular daily time series where some days are missing, e.g. holidays and weekends. Suppose data is a zoo object in ...
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21 views

Scale dataset in R [duplicate]

I have 528 observed temperature data. The values of the data range $[2, 53]$. I have to scale the data to make it range between 0 and 1 to fit a beta distribution to find whether the beta distribution ...
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How can I test which timepoints are different between treated groups and control group?

Which test should I use to find out which tested days are sigificantly different between treated (red and blue) and control group (dashed) ? I don't want full cross-testing between all categories, ...
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How can I get a GraphPad-like result output with R using a two-way mixed model ANOVA?

Design of the experiment is as following: 11 groups of 3 mice (10 groups each infected with a different virus and 1 control group), so a total of 33 different mice 15 tested time points, from day-6 ...
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Normal distribution and independence

I was reading about white noise and it stated: Although $\varepsilon_t$ & $y_t$ are serially uncorrelated, they are not necessarily serially independent, because they are not necessarily ...
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Using a Lag Variable in Time Series Data

I am new to Time Series Data and this question is confusing me, as I have received different advice and was wondering if I could request clarification. I am attempting to test whether the creation of ...
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26 views

Avoiding spurious correlation in time series

I am investigating the relationship between monthly macro-economic variables, and monthly indicators of a company's performance and workload. I am doing this for predictive purposes, and I am ...
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Time Series/Dynamic Problem: Is this an appropriate way of accounting for form/recent trend in my model?

I am attempting to conduct a dynamic or time series regression for a tennis analytics project, endeavoring to predict the probability of a player winning a point in which he is the server. For a ...
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Number of Days in a Monthly Forecast

So for the last few months I've been doing a lot of forecasting for my company and specifically I've been looking at monthly forecasts of total weight of different categories of products output's each ...
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What is the advantage of transforming variables into First Difference of the Natural Log instead of % change from one period to the next?

I am dealing with macroeconomics time series data, and I build econometrics models. I am aware that some econometrists like to transform such variables as the First Difference in the Natural Log ...
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how can I use state space models for two dependent systems?

I use a space space model on my data and estimated its parameters via Kalman filtering. Now I have to expand it to two datasets. It means when you have two state space models and you want to see the ...
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Variable selection for Arimax model

I have an econometric dataset with ~350 var and 52 observations. In order to pick suitable variables, we ran univariate regressions and picked the top ~30 candidate predictors. Next we ran a variable ...
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Anomaly detection within periodic time series [duplicate]

I would like to detect anomalies in a time series data using a moving average approach. Basically, the system will rise an alarm when a data point is far away (3*std for example) from the current ...
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1answer
41 views

Regression or time series?

I need to predict the sales of a product P2. I have access to: 7 months of sales history 26 months of sales history of another product P1 I assume the sales trends are similar because the ...
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What techniques can be used to predict a time series with another time series?

What techniques can be used to predict a time series (say monthly economic data) with another time series (say a company's sales)? If you only have about 50 data points of monthly data, and a yearly ...
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How to determine stationarity, mean and covariance?

I'm having some trouble with some questions for an assignment that I need to do. The question asks to determine whether or not a process is stationary and if it is, what is its mean and covariance. ...
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How to train radial basis function for function approximation?

There is an Autoregressive model of order 1 (AR(1)) that is excited by a non-linear signal as the input: $$x_t = \rho x_{t-1} + u_t \tag{1}$$ The time series $u_t$ is generated from a Mackey-Glass ...
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Moving Average (MA) process: numerical intuition

This forum is full of questions regarding MA processes; for instance: Confusion about Moving Average(MA) Process. There seem to be a lot of confusion wrt MA processes. I think having a numerical ...
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Variable selection in time series data

I have an econometric dataset, 50 observations of 350 variables. They include things like GDP, unemployment, interest rates and their transformation such as YoY change, log transform, first ...