Questions tagged [time-series]
Time series are data observed over time (either in continuous time or at discrete time periods).
14,321
questions
1
vote
2
answers
17
views
is it possilbe to run AR(1) model in R with ar is bigger than 1 [closed]
is it possilbe to run AR(1) model in R when ar(which is pie in AR model) is bigger than 1
i know that arima.sim can run AR(1) model with following codes
...
- 11
0
votes
0
answers
5
views
Seasonality in ECM: Controlling Within the Model (e.g., Adding Dummies) vs. Outside the Model (e.g., Seasonal Adjustment)
When running an Error Correction Model (ECM) with seasonal data, two main strategies are typically considered (for example here and here):
Incorporating seasonal dummies within the model to control ...
- 21
0
votes
1
answer
18
views
Unsure about assumptions of linear model with time series variables, spurious regression and periodic patterns
Background
I'm learning about time series in context of linear regression. The goal of this question is to understand how seasonality of either X or Y can affect the model.
Linear model assumptions
$...
- 251
3
votes
0
answers
18
views
Determine a robust trend from noisy time series data, when start and end years have a material effect
I have about 20 years of data, each year has a number of observations. If I put a linear trend through the data, I get a trend, and this trend differs based on the the choice of start and end year, ...
- 131
0
votes
0
answers
9
views
How to estimate a date given data on past dates?
I am working on a free visa timeline tracker to let people estimate how long the UK Home Office will take to respond to their applications. I don't know much about statistics, only basic school ...
- 101
0
votes
0
answers
6
views
Forecasting positive time series with missing values and few observations
I am working on forecasting a positive time series dataset of prices with missing values and only a limited number of observations. Specifically, I have 150 observations, one per week, and I need to ...
- 1
1
vote
0
answers
29
views
Is stationarity always an requirement for Spearman correlation between two time-series?
I would like to know if time-series data always (in every case imagineable) has to be stationary before computing the Spearman correlation between two time-series. I subsequently explain my specific ...
- 235
0
votes
1
answer
33
views
Contradictory Sources on Seasonality being a nonstationarity
I have been trying to figure out whether seasonality means nonstationarity, and the answers from many (often reliable) sources seem to be contradicting. (lets define stationarity as weakly ...
- 19
0
votes
0
answers
10
views
Modeling non-negative time series with square root decay?
Q: How should I model a non-negative time series $y_t$ which exhibits square-root decay? More specifically, a time series $y_t$ whose square-root differences $\sqrt{y_t}-\sqrt{y_{t-1}}$ are linear and ...
- 2,127
1
vote
1
answer
10
views
Rolling forecasts where horizon is larger than step-size
Is it bad practice to perform rolling time-series forecasting where the forecast horizon is greater than the step-size? For example, if I have a model which produces weekly forecasts on a rolling day-...
- 105
0
votes
0
answers
10
views
What is the spectral density matrix, in the context of vector stochastic processes
I cannot seem to find any good/easy to read resources on the spectral theory, and in particular for multivariate stochastic processes.
I want to know:
Any resources explaining spectral theory
...
- 349
0
votes
0
answers
25
views
What does a significant PACF and insignificant ACF plot mean about autocorrelation?
I am new to time-series analysis and have been trying to understand how to correctly identify different types of autocorrelation in my seasonal data. I have run a gam and am now looking at the ...
- 1
0
votes
0
answers
25
views
Methods for Data Reduction [closed]
Subject: Methods for Data Reduction in Emission Rate Calculations
Dear Community,
I have conducted numerous measurements in a barn to calculate annual emission rates (such as ammonia, methane, etc.). ...
1
vote
0
answers
9
views
Why does GAP at the end of FCN for MTSC work?
I have a binary MTSC (Multivariate Time Series Classification) problem where i train a CNN, namely a FCN (or Fully Convolutional Network) to predict class 0 or class 1 based on a multivariate time ...
- 11
0
votes
0
answers
22
views
Forecasting Square Waves
I am involved in a social experiment with other college students. The experiment involves simulating current price of the market at which a given asset can be bought or sold for immediate delivery. We,...
- 39
0
votes
0
answers
10
views
Can we use decomposed time series in a test set to obtain models accuracy metrics?
Currently I'm cross validating (rolling time window, different test lengths) several forecast models to obtain performance comparison on highly skewed time series (due to true ocassional outliers). My ...
- 481
0
votes
0
answers
8
views
Feature dependence in Random Forest application
I'm applying a Random Forest Regression on target variable y, number of items bought
At the time of running the regression, I will have access to the 'running total'...
- 1
0
votes
0
answers
19
views
How to determine statistical significance for a time series and forecasts?
With a simple example of mortality rates, and a basic three-year mean baseline:
...
- 101
2
votes
2
answers
33
views
Forecasting a series that comes with uncertainty
I have a time series resulting from a spatiotemporal aggregation on the spatial domain.
As a result, I have a central measurement (let's say mean average) and a dispersion (let's say standard ...
0
votes
0
answers
15
views
How to deal with not continuous stock price data? [closed]
I have stock price dataset as below:
...
- 113
0
votes
0
answers
17
views
Singular Spectrum Analysis Decomposition on single input signal using PyTS module
I read this paper and was curious to apply it on a single-channel audio recording of mixed sources. It is about Singular Spectrum Analysis (SSA).
The paper mentions that a key component of the ...
1
vote
1
answer
36
views
Noise Removal for Consistent Anomaly Detection in Multi-Dimensional Time Series Using Matrix Profile
In an online anomaly detection task involving multiple time series, I compute the left matrix profile using non-normalized Euclidean distances for each of the time series (Figure 1). However, since ...
- 173
1
vote
1
answer
18
views
How to deal with zeros when using NBEATS to forcast demand?
So, I'm doing forecasting demand for a hotel using NBEATS, which is hierarchically organized. However, I'm facing an issue with the time series data at the bottom (room numbers), as they're filled ...
- 111
2
votes
0
answers
24
views
Appropriate stats model for time data with an upper limit?
I'm struggling to settle on a statistical approach for a portion of my dataset. Any thoughts/insight would be appreciated.
Subjects (divided into two categorical groups) were given up to an hour to ...
- 21
0
votes
0
answers
29
views
Forecasting RNN and LSTM without X_test
Dear StackExchange Community,
My data is composed of only 1 time series variable (Stock prices of an asset)
I have splitted it to train and test subsets.
I have tarined an RNN and LSTM models with ...
0
votes
0
answers
13
views
What kind of tool can be used to create a stationary time series from linear combinations of multiple time series?
If I have several time series datasets, what kind of tool could be used to create a stationary time series from a linear combination of these time series?
I suspect that several tools are required to ...
- 276
1
vote
0
answers
54
views
How adjust regression coefficient by sample size?
Suppose I have two time series, $\{X\}_{t}$ and $\{Y\}_{t}$ with significantly different lengths, $n$ and $m$ observations, respectively ($n>m$). I want to estimate the following AR(1) models:
$$X_{...
- 281
2
votes
0
answers
17
views
when doing an STL decomposition, is is possible to take the first derivative and get a confidence interval around it to test if different from zero?
when doing an STL decomposition, is is possible to take the first derivative and get a confidence interval around it to test if different from zero?
I know this can be done with GAMS as shown in this ...
0
votes
0
answers
10
views
Please can I have some guidance on analysing policy change on time series data?
Firstly, I am not a statistician, merely a consumer of statistics (NHS Pharmacist).
I am undertaking a project looking at the impact of a new clinical test on antibiotic prescribing across multiple ...
- 1
0
votes
0
answers
17
views
Time Series Data Stationarity [closed]
I am trying to prepare data for a time series analysis, and I am testing for stationarity. If I have the data at the daily level, it looks like this
with x= daily time and y=some metric.
When I do an ...
- 11
0
votes
1
answer
33
views
Interpolation of errors from model predictions over time-series
I have a regression model:
...
1
vote
0
answers
24
views
Ergodicity-definition for general statistic
I'm struggling with the definition of ergodicity within time series. Consider a time series denoted as $X = (X_i)_{i\in\mathbb{Z}}$, where each $X_i$ represents a random vector defined on the same ...
- 389
0
votes
0
answers
16
views
Does anyone have any suggestions on how to identify changes in streamflow response to precipitation events after a disturbance?
I have a .csv file containing data for stream discharge, stream flashiness, and precipitation over 15 minute intervals from 2010 until 2022. The format of my .csv file is
Date, Time, Discharge, ...
3
votes
1
answer
37
views
How to determine (the lag order of) MA from these plots?
I am using time series to analyze the price of a commodity. Characterizing is the fact that the price of the good is determined per hour, one day prior to the day of delivery. For now, I have ...
- 31
2
votes
0
answers
73
views
Issue with SARIMA model for PM10 concentration forecasting with m=365 [migrated]
I'm trying to build a SARIMA (Seasonal Autoregressive Integrated Moving Average) model for forecasting PM10 concentrations based on five years of data. However, when I set the seasonal parameter m to ...
1
vote
0
answers
23
views
Is stationarity important when using boosting models?
I've studied time series for the past months and I've seen mainly two ways of building a forecasting model:
Using ensemble algorithms and making the time series look like a cross-sectional data, in ...
- 660
3
votes
1
answer
28
views
Why do we make a time series stationary if the ARIMA, AR and other models are clearly working with the dependence of lags?
When we run a AR model, we are using a linear combination of its lags to predict the current value. So this means that the lags are related to each other (at least t-1, t-2, ..., t-n are related to t0)...
- 101
0
votes
0
answers
5
views
Use of composite dependent variable in Economics
I'm curious about research that employs a composite dependent variable in empirical studies. Specifically, I'm interested in analyses that utilize any composite indexing method while taking into ...
0
votes
0
answers
13
views
ADF test output in R
I Run ADF test with R for 3 different models include,
No deterministic terms,
with constant and
constant and trend based on the below code.
However, I am unsure about which corresponding p-value (I ...
- 55
0
votes
0
answers
11
views
Prove the convergence of the sample PACF
I'm looking for a proof that the sample PACF converges in probability to the PACF in probability as T goes to infinity when m = p.
Moreover, I am looking for a proof that when m > p, that the ...
- 1
0
votes
0
answers
11
views
Interpretation of ARIMAX residuals
Plotted a seasonal ARIMAX model where I took one difference and one seasonal difference, am unsure of how do I interpret the residuals output below. The resultant model is ARIMA(2,1,2)(2,1,0)[12] ...
- 11
0
votes
0
answers
27
views
Robust or Stochastic Optimization Approach for Maximizing Profit with Limited Price Information
I am tackling a linear maximization problem where I need to select the optimal product among several options over a series of weeks, given certain constraints, in order to maximize future profit. The ...
- 1
0
votes
0
answers
19
views
Standard deviation of an autocorrelated time series
Given a time series of log returns $R_t$ with significant autocorrelation up to $k$ lags, what is the formula for the standard deviation of $R_t$ that accounts for this autocorrelation?
I've seen the ...
- 123
0
votes
0
answers
11
views
Measures of similarity for time series data
I've got two year's worth of energy data in 15 minute increments, and need to develop a similarity score for a forecasted day i.e. identify past days that are similar to the forecasted day.
I started ...
Beginner
0
votes
0
answers
22
views
Large Scale Missing Data & Imputation of Time Series Data in Neural Networks [duplicate]
I know there has already been a lot of discussion about this topic, but I have reasons to believe it still remains unanswered and lacks several justifications.
Suppose we have an time series feature ...
- 101
2
votes
1
answer
41
views
How find the "closest" (in a sense of data generating process) time series?
Suppose we have overall $m$ time series, each with $n$ observations. We also have another time series with $n-k$ observations ($k>0$). Given the shortest series, I want to find from $m$ series ...
- 281
0
votes
0
answers
7
views
What is the effect of sampling rate on parameter estimation when fitting a markov state model to timeseries data?
Let us say that I have some timeseries data, which can be described by a markov state model. And the time series has been sampled every $\Delta t$ time units. The sampling rate ($1/\Delta t$) must ...
- 1
0
votes
0
answers
12
views
Normalization of time-series data with time-varying variance
I'm building a neural network (CNN) model for a regression problem with time-series data. Both input and output are multi-variate zero-mean timeseries data with time-varying variance. Currently, I am ...
- 111
0
votes
0
answers
18
views
Asymptotic distribution of white noise ACF
I (encountered this in my lecture) wonder why do we want the autocorrelation of our residuals to be mostly within 2 s.d. as a sign that residuals are consistent white noise?
More specifically
why do ...
- 103
0
votes
0
answers
5
views
Difference transformation and Stationarization of Moving Average
I have a temperature sensor data, I want to denoise it. The first thing that came to my mind was to take the moving average, it was very smooth but it is still not stationary. If I take the log ...
- 33