Questions tagged [time-series]

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

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1 vote
0 answers
34 views

Time series dynamic model: can we also learn time varying models?

I would like to train multivariate time series models with time varying weight information (time-varying relationship between data and labels). My understanding is that for example, autoregressive ...
2 votes
1 answer
782 views

Is a likelihood ratio test appropriate when checking for unnecessary lags in a VAR?

If I trying to estimate the proper VAR to do a one period ahead forecast, but I am unsure of how many periods I should lag one of my variables, for example if I should lag it 2 periods instead of 3, ...
5 votes
1 answer
689 views

Are there ML algorithms for data whose probability distribution changes over time?

It seems ML algorithms are specialized for cases in which the population distribution is fixed. Cross-validation also wouldn't work well if the distribution would change over time. However, it is not ...
0 votes
1 answer
470 views

Time varying random variable

I am studying the water surface elevation in the presence of waves, at one location over time $\eta(t)$ as seen in the following figure.           &...
1 vote
2 answers
707 views

How to define a time series classification problem?

I have 3 sets of time series data generated from sensors, I believe they have some correlation themselves. Certain "modes" of the system can be defined from the patterns from these signals. The signal ...
1 vote
1 answer
2k views

Why are AR(p) processes always invertible?

My question is the following: If we have an AR(p) process, then we have the following $$ \Phi(B)X_{t}=Z_{t} $$. I understand that to check for causal/non-causal stationarity, we consider the roots of $...
1 vote
0 answers
70 views

Time Series and non-parametric tests [closed]

Assignment 2 Part C – Time Series and Non-Parametric Tests The number of fishing rods selling each day is given below. Perform analyses of the time series to determine which model should be used for ...
2 votes
0 answers
192 views

Chow-Lin Time Series Disaggregation

Hy, I am working on a time series with yearly observations starting from 1995. Since I wanted to forecast the next values with ARIMA methods, I thought it was more appropriate to get quarterly data ...
1 vote
0 answers
349 views

Pros and cons of converting weekly to daily data

I am trying to forecast an economic variable called the "yield spread" in python. Among the variables in my dataset, two of them are measured on a weekly basis. These are: unemployment ...
0 votes
1 answer
115 views

Why do we need to make a distributional assumption on the innovations $(\varepsilon_{t})_{t}$

Why do we need to make a distributional assumptions on the innovations/white noise $(\varepsilon_{t})_{t}$ for linear forecasting of a time series $(y_{t})_{t}$. In a lecture, we introduced a white ...
1 vote
1 answer
2k views

What's the best (Google chart) visualisation for displaying sparse timeline data across thousands of "columns"

I am trying to visualise a sparse dataset but am finding it hard to fit it into the standard categories of charts. I'm a developer building with Google Charts and I really want to stick with that ...
1 vote
0 answers
25 views

handling multiple time series through common model?

I have 1.5 lac/ 150 K timeseries . These are divided by geo locations. I have total 32 geo locations.Customer is expecting to have minimum number of model for all the 1.5 lac forecasting. How should i ...
0 votes
0 answers
65 views

Account for autocorrelation without autoregressive model

I have a time series model that has seasonality (and therefore it has autocorrelated errors fitting an OLS model) - how can I account for autocorrelation without a complicated autoregressive model ...
9 votes
0 answers
888 views

Why does uncertainty of the autocorrelation coefficient increase as lag increases?

The Python module statsmodels contains functions for ACF and PACF. Below is an example from the docs with a plot that shows the (zero-centered) confidence ...
0 votes
1 answer
40 views

Is my Moving Average model correctly implemented

I am new to time series modelling and I was trying my hands on a dataset which records number of customers per day from 1 jan 2018 to 31 dec 2019. So far, I have tried implementing a naive moving ...
1 vote
1 answer
183 views

Measuring impact of advertising on retail sales

I have a dataset of retail products which contains weekly sales for 12 different items in a single category. For each item, I have three dummy variables representing different types of advertising (...
0 votes
0 answers
22 views

How to De-seasonalize a time series based on trading days? [duplicate]

I have a time series of monthly data points with a corresponding cost. Within these months, if you strip out the trend, I believe the cost to be highly correlated to the number of trading (or business ...
1 vote
0 answers
493 views

Is it possible to convert all non-stationary time series to stationary ones?

According to this question Is every non stationary series convertible to a stationary series through differencing I already know that differencing isn't enough to make every non-stationary series ...
0 votes
0 answers
20 views

regression with time series, wanted value is time independent [closed]

I hope anyone can help me because i couldnt find any Information on this Topic. I´m trying to estimate a value (the weight/mass) with machine learning(regression). The data that i have is a time ...
2 votes
0 answers
2k views

Is the Python Johansen cointegration test trustworthy?

I'm trying to learn how to do Johansen's cointegration test. I am using the Python's "statsmodels.tsa.vector_ar.vecm.coint_johansen". I have run 10 tests, each with 5 series. All series ...
0 votes
0 answers
37 views

Finding the maximum likelihood estimator given a special variance

Assuming that $x_t\sim N(0,\sigma_t^2)$, where $\sigma_t^2=\mu+\beta\sigma_t^2+\alpha x_{t-1}^2$ In this case, what is the likelihood function given the sample data $(x_1,x_2,\ldots,x_T)$? I ...
0 votes
0 answers
266 views

Autocorrelation problem with "time series" data

I have some data in python that I plan to run a linear regression on but first I tested the assumptions of linear regression. When it came to autocorrelation my Durbin Watson statistic was 0.14 which ...
5 votes
0 answers
694 views

time series: What is the performance difference between ARIMA models and Bayesian Structural Time Series models

I have been looking at ARIMA/SARIMA models and some of the Bayesian Structural Time-Series models lately. The formulation of the two models does not seem that different but the fitting method of ...
1 vote
1 answer
75 views

Unable to model an AR(1) process

This is my AR(1) series where X(t) = 0.9 * X(t-1): ...
0 votes
0 answers
328 views

Bootstrap the estimated GARCH volatility....for the two stage estimation

GARCH(1,1) model is represented as \begin{aligned} x_t &= \sigma_t z_t, \\ \sigma_t^2 &= \omega+\alpha x_{t-1}^2+\beta\sigma_{t-1}^2, \\ z_t &\sim i.i.d (0,1). \end{aligned} If I want to ...
0 votes
0 answers
12 views

I have brain wave data for different subects aka voltages recorded every X sec. How do I deal with different wave frequency between subjects?

Brain wave data is really just voltages recorded every certain number of seconds; this becomes a wave with a certain frequency. I want to use the voltages recorded every, say, 10s in a model; this is ...
2 votes
0 answers
71 views

How does choosing an even window size actually add a cyclical component to the model?

I am new to Time Series Analysis. Say, we have a time series $(y_{t})_{t}$ that we want to filter with a moving average filter. I have been told that we should choose the window size $L$ of the filter ...
1 vote
1 answer
217 views

How to compare GARCH model outcomes from two equal time series

I'm writing my thesis and will sketch the scenario I try to research: I have data for my GARCH model from two periods. The input is the same, as is the length (1y). I want to compare both the outcome ...
0 votes
2 answers
986 views

Time Series Decomposition: Is it necessary (or wise) to remove outliers beforehand?

Do outliers change the outcome of time series decomposition? As far as I understand it, outliers occur in the residual-component. In the residuals plot they can be visually identified as spikes. ...
2 votes
1 answer
882 views

Am I conducting this likelihood ratio test (selecting between GARCH and TGARCH) correctly?

I calculated log likelihoods for two times series models, a GARCH and a TGARCH. The GARCH model is nested within the TGARCH, and their respective log-likelihoods are: \begin{aligned} \text{LL_GARCH} ...
4 votes
2 answers
1k views

Show that $z(t) = A \cos(bt) + B \sin(bt)$ is second order weakly stationary process

I know that in order for a stochastic process to be a second-order weakly stationary process. Then for every $t$, the following conditions should hold: $$E(Z(t)) = \mu$$ $$D(Z(t)) = \sigma$$ and $$\...
0 votes
2 answers
493 views

Why does the Augmented Dickey fuller test not use the F-test for checking stationarity

As I understand the F-test is much better suited for analyzing linear regression models, so why does the ADF test not use the F-test rather using the T-test for hypothesis testing.
2 votes
0 answers
964 views

ARMAX with lagged exogenous variables

I have difficulties finding the implementation of ARIMAX, ARMAX model where the exogenous variable would be also included with the time lags: $X_{t}=\varepsilon_{t}+\sum_{i=1}^{p} \varphi_{i} X_{t-i}+\...
0 votes
0 answers
337 views

Error terms and Residual in Time Series

I started learning Time Series Forecasting and the below questions keep my head confused a lot. Many papers mention that the forecast errors have to be normally distributed and what about the white ...
0 votes
1 answer
52 views

Machine Learning terminology to describe when using future data to predict the past values?

In time series forcasting, do we have a terminology to describe that, when we randomly shuffle the data and training on future data and predicting the past values? I mean if we destroy the sequential ...
1 vote
0 answers
31 views

Is it valid to compute the Spearman rank correlation's p-value on summarized (mean/median) data?

I have a dataset generated as follows: On each day for 30 days, a measurement is made for each individual. Individuals are separated into two groups. Each group contains 5 individuals. Is it valid to ...
2 votes
0 answers
108 views

Pre-Whitening results not conducive to white noise property [closed]

I have these two time series(df_errror,df_booking) (both are non-stationary, seasonal) that I want to prewhiten and then find cross correlation: I used this code for auto_arima for df_booking: ...
-2 votes
1 answer
711 views

No ARIMA model fits my data

I tried to fit an ARIMA model to a data, but no success!! I shared my data and the R-codes below to check any mistake! dt=c(15,18,13,16,11,14,19,20,16,17,13,11,13,15,8,12,15,14,15,15,18,11,13,15,11,11,...
1 vote
1 answer
216 views

Parameters identifiability / estimation in Bayesian linear state-space models

Is it possible to tell if the parameters can be uniquely estimated in a Bayesian state-space models from the system equations (beyond redundant parameterisations). If so, how? For example, should it ...
4 votes
1 answer
400 views

Can autocorrelation confound causal inference?

I'm working with a weekly aggregated time series that has autocorrelation and I'm trying to find out why the trend has been decreasing by regressing other features onto - I noticed that when I use an ...
1 vote
1 answer
1k views

Proof of contemporaneous exogeneity, and its implications for an AR(1) model

It can be shown by contradiction that exogeneity fails to hold for an AR(1) model. Is there any proof that contemporaneous exogeneity does not fail to hold? All I've come across is assuming it does ...
0 votes
0 answers
166 views

Working with Time Series: What drives a trend change?

I'm working with a time series that had a clear trend changepoint where it went from having an upward trend to a downward trend (After accounting for seasonality). The problem that I'm running into is ...
0 votes
1 answer
110 views

joint p.d.f. of stationary time series variables

if a stationary time series verifies that each variable depends only on the variable before it, and the joint p.d.f. of $x_i$ and $x_{i-1}$ is $f(x_{i-1},x_i)$, which is the joint p.d.f. of $x_i,x_{i+...
1 vote
1 answer
160 views

forecasting with optimised theta method (otm) using time series cross validation with R

I want to do an out-of-sample forecast experiment using the optimised theta method (otm) on a time series. Further, time series cross validation with a fixed rolling window size should be applied. ...
2 votes
1 answer
1k views

How can I plot presence/absence data on a time series?

I have some schedule data in the following format ...
0 votes
2 answers
2k views

Time Series with no Autocorrelation

I have a time series. I plotted it and saw that it is not stationary. Thus, I have calculated the difference. Then I plotted the autocorrelation and partial autocorrelation on the differences, along ...
1 vote
2 answers
2k views

Is there any standard / criteria of good forecast measured by SMAPE and MASE?

I have built a forecasting model for a company. Since it is dedicated to practical usage, I prefer to use the relative error parameter (like MAPE, SMAPE, & MASE) as a measurement for my model ...
3 votes
0 answers
373 views

How to correctly pre-whiten time series

I'm trying to find cross -correlation between two-time series and as it so happens, they are auto-correlated(2), nonstationary and co-integrated. As I read about them, it appears that pre-whitening ...
2 votes
1 answer
419 views

Cross correlations

I have a question about cross correlations and multivariate time series. I've read several articles and posts on SE about how to properly prewhiten data for cross correlation of two time series, and ...
1 vote
1 answer
40 views

Time series methods to compare quantitative continuous data (e.g., heart rate) and qualitative/subjective data (e.g., self-reported stress)

I work with a lot of data from sports wearables (e.g., heart rate). I ran a study where people wore a heart rate tracker whilst doing an activity. Every 5 minutes they were prompted to rate how much ...

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