Questions tagged [time-series]
Time series are data observed over time (either in continuous time or at discrete time periods).
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Frequency error when running Holt-winter's seasonal method in R
I am trying out time series analysis on R using the "Unemployment Rate" data of the US_indicators dataset in the TSstudio package. As the dataset originally exists as a data.frame, I tried ...
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VAR in levels of differences when series are integrated but not cointegrated?
I have monthly financial time-series data from 2011-present of four stock market indices. I conducted various stationarity tests and found that the series are I(1) processes (stationary only in first ...
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Time series linear regression: does the violation of strict exogeneity imply correlatedness?
If we have the linear regression model
$$y_t = \mathbf{x}^\prime_t \mathbf{\beta} + u_t$$
for time series data, for which we know that the strict exogeneity assumption is violated, i.e., we know that
$...
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ARMA and AR processes
I am taking time-series econometrics this semester and got stuck with the following. Assuming we have $ARMA(1,1)$ model: $Y_t = 0.2Y_{t-1} + ε_t + 0.1ε_{t-1}$ with the estimated variance of $1$.
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Can anyone suggest minimum and maximum values that the relative entropy/KL divergence can range when computed from two time-series sequences?
Can anyone suggest minimum and maximum values that the relative entropy/KL divergence can range when computed from two time-series sequences?
I have calculated the relative entropy for two time-series ...
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Clustering time series of unequal length using DBSCAN and DTW
I'm working on a dataset of all visits generated by email campaigns that were sent in 2020, and the goal is to develop a clustering model that groups similar campaigns (trend line similarity) together ...
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Why is AIC or BIC commonly used in model selections for time series forecasting?
On scikit-learn documentation, I found the following comments about AIC:
Information-criterion based model selection is very fast, but it relies on a proper estimation of degrees of freedom, are ...
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Forecasting in Time-Series [duplicate]
We want to predict Y based on some function of X, i.e., Y_hat = f(X). How can we show that the conditional expectation f*(X) = E(Y|X) is the mean-square optimal predictor, i.e., the function f* solves ...
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Conditional mean and orthogonality
I am stuck with the proof that the conditional mean satisfies the orthogonality conditions. Say, we have Y as a scalar random variable with finite variance and X as random vector. Conditional mean of ...
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Equivalent of "lm" for irregular time series forecasting in R
I have a two column data frame corresponding to time series of the form (Date, Value). I want to predict future values of Value based on this data. I don't need anything fancy, just a quick and dirty ...
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Dynamic OLS: serial correlation in the residuals
I want to estimate cointegrating vectors between different I(1) time series. I used the R package CointReg to run D-OLS regressions (function CointRegD).
The number of lags and leads was selected by ...
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How to do low scale time series prediction? [duplicate]
I have a small time series data with 10 observations
Each observation is spaced at 20 days gap
For example, I have sales revenue from day 1, day 21, day 41, day 61 till day 221...
Now I would like to ...
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Summation of median and quantiles of multiple forecasted variables
Assume that I have Y1_hat with its P10_1 and P90_1 and Y2_hat with its P10_2 and P90_2.
Is it valid to sum Y1_hat and Y2_hat, sum P10_1 and P10_2, and sum P90_1 and P90_2? and would that present any ...
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Meta/ Few-shot Learning for time series regression
I am working on the calibration of low-cost air sensor data (a time series regression problem). My primary focus is to use some meta/ few-shot learning approach to solve this problem with a lesser ...
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Parameter simplification of ARIMA model
I am constructing an ARIMA model on a cryptocurrency price time series. Using the autocorrelation and partial autocorrelation plots I came to the parameters of (p,d,q)=(3,1,2).
The resulting RMSE was ...
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How can I account for large lag cycles in timeseries regression with ARIMA errors?
I'm trying to create a model and generate a forecast of energy consumption in an HVAC system at the university I attend. I have energy consumption for the system and some basic weather data. Sample of ...
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SARIMA vs SARMA model
I've built two time series models using the same data.
One model is a SARIMA (1,1,1) (0,0,0,52) model built with at level data and the second model is a SARMA (1,0,1) (0,0,0,52) model built with first ...
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Unable to interpret p statistics for periodicity testing of signals [duplicate]
The meaning of P value is probability which should be number between 0 (the event never occurs) and 1 (the event occurs always). significance testing for periodicity using Matlab gives a documentation ...
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Box-Cox, Exogenous Variables and Time Series Models
I am building time series models using SARIMAX from Statsmodels (Python).
The independent variables in my models include 3 to 5 exogenous variables that are other than the target variable I am trying ...
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2
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524
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Lasso Regression to get most important factors?
I'd just read about Lasso-regression and would like to ask if the following approach would correct from a statistical point of view.
So given I've a list of genes and would like to observe their ...
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How can I use survival analysis with multi-state model to look at a progressive events (gentrification stages)?
I'm working examining the effect of a particular anti-gentrification policy in Berlin. I want to use a survival analysis to see if the amount of time it takes any area to progress to the next stage of ...
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Detect regularity in arrival times
I am working with series of arrival times. My typical dataset is made of 20-100 samples.
I would like to detect regularity in the arrival time.
By regularity, I mean that the inter-arrival times may ...
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Timeseries OLS Coefficient Interpretation - Log Sum Transformation
This should be straightforward and I apologize in advance but for some reason, a colleague and I are in disagreement over the interpretation of some regression coefficients. Suppose we have a time-...
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ARMA Models with Trend Question
I am looking to build an ARMA model for a time series with a significant trend component.
Let's assume for the purposes of this question that I don't want to build an ARIMA model. (The reason has to ...
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how can i analyse this time series [closed]
How can i analyze this weird time series, i have no idea where to start ( stl decomposition ?, arima? or may be something else ...)
in brief, I am new to time series analysis and haven't developed any ...
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Where can I learn the math behind how airlines dynamically price tickets?
It's easy to think of factors that airlines take into account, and how the price will vary over time. There are no shortage of articles or medium posts about that. But how can I frame this as a ...
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CLT in Diebold & Mariano (1995)
The Diebold-Mariano (DM) statistic is derived as follows:
Assuming the loss-differential between the two models $d_t$ is covariance stationary:
$$\begin{cases}
\mathbb{E}[d_t] = \mu> 0 & \...
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Topic modeling with time data
I want to predict the topic of a project tracker narrative entry to identify stages of the project. This entry is written by employees describing what they did in the project.
The projects are ...
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1
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694
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How to find the order for ARMA model?
I have a problem finding the order with the ACF and PACF plot, below is it.
First I think they can be considered as tails off gradually because they are abnormal, then I set AR(1) from PACF and MA(1) ...
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How to handle the missing values in newly listed stocks?
I am trying to test some asset pricing models on 10 portfolios for the period of 2010-2020. The problem is that three of these portfolios included stocks that are newly listed in 2017 and 2018, so I ...
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RNN Sequence Prediction - Am I Introducing Leakage?
I am training an LSTM for sequence prediction where the targets are either 0 or 1 and I am currently using a sequence length of $20$. I have done extensive feature engineering so I have 61 input ...
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Are consecutive zeros across multiple dimensions in multivariate time series a problem when estimating VARMA models?
I want to estimate a VARMA model for a 14-dimensional multivariate time series (Fig. 1, 2). The goal is to investigate how the trajectory of my alleged output time series (messages per hour; Fig. 1, ...
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Why ARIMA Ljung-Box p-values tend to decrease for large lag
I am a beginner at ARIMA model fitting. I have observed that after I fit my model, the Ljung-Box statistic tend to discard the null hypothesis for large lag. It could be that it is only random that it ...
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How to interpret the Score-Based CUSUM test results?
Context
I'm doing an analysis of a price time series and checking for structural breaks (s.b. further on). One of these tests is the Score-Based CUSUM test. As far as I understand, this test is more ...
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How to interpret the coefficients in a time varying linear regression model?
How do I interpret the coefficients in a time varying linear regression model (in particular theta2).
Formula from the book “Dynamic Linear Models with R”
As I understand, at time t theta2 is the ...
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Testing the coefficients of the same linear regression model during two distinct time frames
I searched for this question in the forum but couldn't find any prior posts that match my question. I am currently working on a linear regression model that has 5 independent variables (metric values ...
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Intuition of Random Walk having a constant mean
I am very new to time series analysis.
A random walk is defined as $Y_t=\phi Y_{t-1}+\varepsilon_t$, where $\phi=1$ and $\varepsilon_t$ is white noise. It is said that process is non-stationary for ...
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When using longitudinal variables on different time scales in a regression, is it valid backwards fill the dependent variable?
I'm working on a longitudinal project that is assessing an outcome variable through a monthly questionnaire and using daily activity as a predictor. The questionnaire asks about symptoms in the past ...
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Anomaly Detection in Highly Variable Time-Series Data
I am trying to detect anomalies through a column called count. The data is a time-series data and it is present for every 5 minutes for each day. The dataframe looks like this:
...
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2
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SARIMA with a year to year dataset
I have a time series dataset that has yearly data points, and we can see that there is recurring pattern every few years
My question is : can I use SARIMA with this model, by changing my dataset as a ...
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Distance from centroid infinite in time series clustering (K-Shape)
I am trying to do time series clustering with R library dtwclust, and more specifically the k-Shape algorithm. I have almost 100,000 time series with 28 time points ...
2
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2
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Why Engle-Granger cointegration test states cointegration between two variables but their trend in time is almost identical?
I have run the Engle-Granger cointegration test in Python between a stock's return and its index return. In this case i have run the test between QQQ returns vs CCMP returns like this:
...
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How to compare two VAR time series models in R?
I have generated two VAR time series models in R for a dataset. My query is how can I compare those two models based on any kind of metrics like forecasting power :accuracy / f-1 score or something ...
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2
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Optimal window size for contextual outlier detection
I am looking for methods to detect univariate contextual outliers in time series data. One example application is data from industrial plants in different (unknown) operation modes or slow trends or ...
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2
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ARIMA model with least AIC giving negative forecasts even though there are no negative values in the training data
I'm training a arima model on a daily time series data where I'm trying to forecast daily inflow counts of a request on a particular day. It can be either positive or zero (no zero requests in the ...
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Regression line Scatterplot R (Time Series)
I am trying to do an interrupted time series analysis, while my knowledge about this is quite limited, though I dig deeper and deeper every day.
I am interested if the price of a certain wine is ...
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1
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Compositional Data with Time Series
I have time series data corresponding to different entities. My goal is to train a model on the set of entities I have, and then provided a new entity to predict the whole time series for it.
For ...
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2
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Time series forecasting - Residuals not white noise
This is my first message on CrossValidated to get some insights on an issue I am facing while trying to model properly a time series. I am relatively new to this science so please brace with me.
My ...
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Forecast is simply equal to the lag of the original time series
I am currently dealing with the problem of short time series which often involves naive models as they already perform well enough. So I implemented an exponential smoothing that follows
$$
F_t = \...
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For a time series, does finite variance imply finite autocovariance?
If variance is finite at all times, does this imply that all pairwise autocovariances are also finite?