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

learn more… | top users | synonyms

0
votes
0answers
14 views

Creating auto arima for two following time series with two different non leaner slopes

I'm trying to model (and predict) the following time series, which consist of two periods (enrollment period and non enrollment) as the following: I believe that this model should consist of two ...
-3
votes
0answers
25 views

Time series / Panel analysis [on hold]

My data in EViews is in on generating all variables from varibleA2002 to variableC2012. I didn't enter company names from excel into ...
0
votes
0answers
10 views

model specification for SARIMA order (2,1,2)x(0,1,1) period 12

Good morning scholars. Please am fitting a Seasonal Arima model of this form: (2,1,2)x(0,1,1) period 12 but I don't know how will look like. Can anybody help me wit the model specification? Thanks.
0
votes
0answers
17 views

Fitting ARIMAX with lagged X variable (Matlab)

This question is divided into two parts. I currently have a Y vector with 364 data points (Y) and an exogenous variable (X) with 364 data point. X is a good predictor for Y that I want to pair up ...
0
votes
0answers
13 views

What does a fitted value mean in dshw forecasting package?

I have a double seasonal data. I wrote the following code to find the best fit model and find fitted values: orders <- read.csv("DataForR.csv", header = TRUE), NumOrders <- orders$Orders, ...
1
vote
0answers
29 views

Quantity like correlation

I want to calculate this sort of quantity, $f()$, for my data. $x$ and $y$ are time series. $f$ behaves like a pseudo-correlation, but is different in the sense that even if the values jump up and ...
0
votes
0answers
10 views

Transforming a time series with a negative number

I have been given data to forecast however it has a negative figure within the data which then, when doing a log transformation to make the series stationary, the ARIMA script i have written won't ...
1
vote
1answer
38 views

Regressing a differenced variable on a lagged variable. How can I fix the error in R?

I have a time series (std) of 324 observations with no missing values, starting from January 1987 and ending in December 2013. I want to regress via OLS the one in the question. In R, the code: ...
0
votes
0answers
12 views

ARIMAX model or ARDL?

I would like to study the impact the advertising of a product on its sales (weekly data for 5 years). As the final aim is to forecast what would be the impact on sales of a change in the advertising ...
1
vote
0answers
16 views

Initiator follower analysis with time series data sets

I am a newbie to this forum. I searched different white papers and codes on google but couldn't find a solution, that's when I registered on this forum.. Please share in case you guys have a idea as ...
0
votes
0answers
10 views

Python module request: Spectral density estimation for multivariate time series

I have worked with scipy.signal.welch and spectrum.pptm to calculate power spectral density with Welch and Multitaper methods. However as far as I can see these functions are meant for one dimensional ...
0
votes
0answers
15 views

Time-series detection algorithm for multi-seasonal data using Python

My data: I have two seasonal patterns in my hourly data... daily and weekly. For example... each day in my dataset has roughly the same shape based on hour of the day. However, certain days like ...
3
votes
1answer
27 views

Is there a method to disentangle multiple lines of data that are intermingled?

I have 3 temperature sensors that record data once a minute. All 3 temperatures have the tuple value (instant, temperature). The problem is, they may come in a random order and thus there's no way to ...
0
votes
0answers
39 views

a representation of ARMA(1,1) process

let $X_{t}=\frac{4}{5}X_{t-1}+u_{t}$ with $u_t$ a white noise with $\textbf{E}(u^2_{t})=\frac{9}{50}$. assume that $X_{t}$ is affected by ameasurement error,and let $Y_{t}=X_{t}+v_{t}$ where $v_{t}$ ...
1
vote
1answer
28 views

prediction of polls

Just as an example Scotland has poll to decide whether they need to be independent from UK or not. Here is BBC's summary of different polls: ...
0
votes
0answers
4 views

Choose best time window to count events in order to produce an indicator

We want to create indicators for event based clinical conditions, like migraine or epilepsy. This conditions are characterized by events which can happen with various frequencies and we would like to ...
1
vote
1answer
34 views

Residual Value Prediction For Used Electronic Products

I am trying to predict the long term residual value of a product with only the releasing price. I have collected some data off the Internet related with one phone type, and it is pretty obvious that ...
5
votes
2answers
110 views

Average and standard deviation of timestamps (time wraps around at midnight)

I have lots of sensor data with timestamps like "2014-09-09 16:10:45" and accompanying sensor readings. To get some insight into these I want to find "unusual" events by looking at the average and ...
0
votes
0answers
13 views

Calculate standard error in state space model in R

I am estimating a DFM in state space form in R. I have used the function spg from the package BB (optim was not working) and dlm to optimize so now I have the parameters of the filter. I now would ...
0
votes
2answers
98 views
+100

Forecast accuracy calculation

We are using STL (R implementation) for forecasting time series data. Every day we run daily forecasts. We would like to compare forecast values with real values and identify average deviation. For ...
0
votes
0answers
10 views

Sample autocovariance non negative definite

Let $\hat{\Gamma}_k$ be the k dimensional sample autocovariance matrix. I am trying to prove this is nonnegative definite. The first step in the proof is to show that if $\hat{\Gamma}_m$ is ...
4
votes
0answers
61 views
+100

Test to distinguish periodic from almost periodic data

Suppose I have some function $f$ fulfilling some reasonable conditions like continuity. I know the exact values of $f$ (because the data comes from a simulation) at some equidistant sampling points ...
0
votes
0answers
24 views

Predicting one daily variable from another in SPSS

Note: There are similar questions to this one, but they don't seem to get at quite what I'm trying to figure out. I have a week's worth of daily data with a number of variables, including nighttime ...
2
votes
1answer
21 views

standard errors of the fitted values of a time series regression

I really want to understand how the math is working here. I am trying to get the standard error of the fitted values for a time series regression model.In the non-time series regression,I know I can ...
3
votes
1answer
61 views

Unable to understand derivation of Expectation Maximizaton

In Paper, System Identification using Symbolic Chaotic Sequence, Authored by A. Kurian and H. Leung download link under section II B, can somebody please explain ...
0
votes
0answers
24 views

Matlab: Unable to plot partial autocorrelation plot

For a time series I wanted to plot separately the partial auto correlation. Below is the graph for a time series which shows PACF plot of the time series $x$ which I wanted to reproduce: This ...
0
votes
1answer
38 views

How to solve this formula in R for specific days for the whole year? [closed]

I'm a beginner in R. I'd like to calculate the load for 3 water quality parameters in R for specific days for the whole year using the following formula: Using the previous formula, I'd like to ...
3
votes
2answers
118 views

How to estimate model with both linear and exponential parameters?

I have a theoretical growth function that can be perturbed by events, and I'd like to estimate the growth parameters as well as the perturbation, and the rate of falloff after that perturbation. I'm ...
1
vote
1answer
20 views

Is it still considered time series if one uses additional signals

Apologies in advance, time series is not my strength. Say I want to predict f(T+1) using f(T-1, T-2, ..., T-N) -- for example using a multi-level preceptron. If I want to enhance this using some ...
1
vote
1answer
24 views

Autocorrelation summation identity

Let $X_t$ be a weakly stationary process with mean $\mu$ and autocovariance function $\gamma$. How do I show that $$n^{-2}\sum_{i=1}^n \sum_{j=1}^n cov(X_i, X_j)$$ equals $$ n^{-2} \sum_{i-j=-n}^n ...
0
votes
0answers
25 views

Time Series Promotional Effectiveness

I'm trying to model the impact of two promotional tactics that ran in parallel for an year, I have the sales information at a month level and couple of metrics corresponding to the promotional tactics ...
4
votes
1answer
58 views

Product price prediction - include important external factors

I need some hint over what is the general prediction solution to modelling products prices in such a case: I have several models (types) of the product I want to predict prices for each of these ...
0
votes
0answers
15 views

Testing significance of a treatment inducing correlations over a time series

Example data In the example dataset, there are 3 distinct biological measurements, over 3 time points (0,12,24hrs) for 19 individuals. These individuals have been divided into 2 groups: treatment and ...
2
votes
1answer
39 views

Modeling time series data that is bimodal and non-Gaussian

I'm trying to model time series data that is bimodal and non-Gaussian. The 2 modes are due to weekday points versus weekend points. I keep thinking that I just need to split the data up to model ...
0
votes
1answer
37 views

Removing Time-Series Variance from Panel Data

We are working with panel data. But we want to study only the cross-section part of the panel data. So can anybody please tell me how to do any kind of data transformation, so that I can remove the ...
4
votes
4answers
172 views

Predicting time to finish

Out of curiosity, I want to understand how to model this problem. I've been hearing people suggest the use of linear regression but I am not sure how to encode this problem (included my attempt below) ...
0
votes
0answers
23 views

Rule of Thumb for minimum length of time series for Autocorrection estimation

I had a related question answered here: Rule of Thumb for minimum length of time series for AR(1) estimation However the answer gives rise to a new question. I want to be able to estimate the Auto ...
4
votes
0answers
32 views

What is the error on measuring the phase of a sine wave? [duplicate]

Let's say I have a wave, with frequency $\omega$ and phase $\phi$, of the form: $$y(t)=1+A\sin(\omega t+\phi)$$ where $A<1$. I have $N$ measures of $(\hat{y}_i, \hat{t}_i)$, that are assumed to ...
0
votes
1answer
23 views

Data transformation

I was writing with a question regarding a time-varying state space model of the form: \begin{align} y(t) &= \mu_1(t) + A(t)x(t) + v(t); &v(t) &\sim (0, R(t)) \\ x(t) &= ...
2
votes
1answer
29 views

Rule of Thumb for minimum length of time series for AR(1) estimation

I have a data set of 350 points, I want to estimate the lag 1 auto correlation for different sub-sets of the data. More precisely I want to take non overlapping windows of length 1,2,3....n and ...
0
votes
0answers
18 views

Most efficient vector construction for Dynamic Time Warping

I'm in the process of folding FastDTW into my SVM and the question now is how to best format my data (irrespective of normalization). Here's an example of what I'm attempting to do - given two 3d ...
0
votes
0answers
31 views

Financial time series model

I have an interesting question that I think has not been asked yet here. I am building an AI that has as goal to predict how wrong a standard based-on-history model is. This is done based on Natural ...
0
votes
0answers
9 views

unconditional volatility from an Arma-Garch process

I know that one can easily get variance (unconditional) of a Garch (r,s) process : $\sigma^2= \frac { \alpha_0 } { (1- \Sigma_{i=1}^r \alpha_i - \Sigma_{j=1}^s \beta_j ) }$ However I am struggling ...
0
votes
0answers
14 views

How to fit an ARMAX model with more than one exogenous time series?

I am trying to fit an ARMAX with two exogenous time series with the following code but it gives me an "computationally singular" error. I know it is about defining more than 2 time series for ...
1
vote
0answers
14 views

What is the “scale” parameter in “continuous autoregressive model” in cts package?

I am trying to use the "car" command in "cts package" in R program and I see the "scale" parameter there. I wonder whether this can be assumed to be equivalent to time intervals for time series ...
1
vote
0answers
28 views

Bias in lagged dependent variable [duplicate]

$$ y_t = θy_{t−1} + u_t \\ t = 1,...,T; $$ I need to derive a formula for $y_t$ and show that $$ E\left[\frac{\Sigma y_{t-1}u_t}{ \Sigma(y_{t-1})^2}\right] \neq 0 $$
3
votes
1answer
36 views

Modeling a non-stationary bounded series

I'm trying to model a time series variable that represents a percentage, strictly bounded between 0 and 1, that is also non-stationary about the mean. Is there a model form that is able to account ...
0
votes
0answers
12 views

Is time-delay embedding/attractor reconstruction used in some machine learning algorithm?

I try to model/forecast blood glucose levels from my diabetes diary, so I have to deal with some 5-7 daily measurements of estimated carbohydrates, physical activity, insulin doses and measured blood ...
3
votes
1answer
120 views

Doubts in linear regression

If a linear regression model has a constant term say 1 or 0.2, for example if the original model is $y(t) = 0.2 + ay(t-1) $, then what does this constant term imply? Will it hamper the estimates if ...
1
vote
0answers
23 views

Arima model for non-negative data

I have been reading a tutorial for an introduction to time series. It contains a dataset, with an $Arima(2,0,0)$ forecast along with a 80% and 95% prediction interval. It looks like this: This ...