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

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Help in understanding a clustering technique using neural network

I am having difficulty in understanding a technique for clustering and segmentation of biomedical images using the concept of time series. The paper on which the Question is based is : M. Lacomi et. ...
3
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11 views

Criteria for hashing / reduce dimensionality

The data series is obtained from a nonlinear dynamical system. The image shows that a window of length $L = 3$ i.e., a word of length 3 is converted to decimal number from the generated symbolic ...
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1answer
16 views

Test for significance of peaks (maximum) in time series

I have a time series of values, something like this: ...
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31 views

Testing for discords in seasonal time series data

I'm trying to find a way to detect discords in seasonal data. I have an algorithm that can select the most likely sub-sequence to be a discord, but what I'm missing is an actual test. I know that ...
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1answer
17 views

Grouping data in a multiline chart mean + outliers

I have an existing multi-line graph that displays time series data about success percentages of nodes in a cluster in 5 minute intervals, there are more than 50 nodes in the cluster and the way this ...
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0answers
70 views

Testing difference between two portions of a time series with Chebyshev Theorem

I have a time series which presents two different patterns during time. These patterns are related to two different events that happen during the experiment. I can manually select the temporal ...
2
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27 views

Standard deviation vs Stardard error of sample mean

I am currently attempting to analyze some data, and it has been a few years since I have taken a statistics course, so I am a bit rusty at this stuff. I have a times series of 12 different sample ...
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15 views

How can stacking be used to combine time series forecasts?

In classical classification problems, stacking means that some model is trained from the results of a number of previously learned models. Cross-validation is commonly used here. How would you do this ...
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1answer
50 views

How to identify best Model for univariate time series data?

I have a time series data- 53.97 63.32 57.06 60.27 69.46 75.08 78.31 73.28 85.84 69.34 62.57 60.11 55.63 47.29 61.22 58.46 66.26 59.71 51.12 39.36 51.89 ...
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46 views

Time Series Analysis vs Linear Regression for GDP data?

I am trying to build a simple econometrics model that uses urban population, total factor productivity among other things to predict future GDP of a country. First I approached the problem by using ...
2
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1answer
41 views

Predict (un)employment variables - very small dataset

I'm new to econometrics (familiar with ML, Python, Data Visualization). I really have no clear idea what model should I use in order to predict (un)employment variables for 2015-2016 (potentially ...
2
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20 views

Time-weighted Pearson correlation

I'm trying to calculate time-weighted Pearson correlation as described in http://goo.gl/HoqwI7 The coefficient is given by $$\rho_t(X,Y) = \left ( \frac{1-r}{1-r^N} \right ) \sum_{i=0}^{N} r^{i-1} ...
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1answer
14 views
0
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14 views

Hidden Markov model - Time Granularity

Is Hidden Markov model sensitive on time granularity? I mean if I train HMM parameters on dataset which time granularity is 1 minute. May I use the transition matrix and emissions distributions for ...
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26 views

Difference between AR(1) lagged models with exogenous variables versus ARIMA(1,0,0) models with exogenous variables

Assume a general time series $y_t$ for $t = 1, \cdots, T$ and a sequence of known exogenous variables $x_t$. Consider the two options: Fitting the parameters $a$ and $b$ of the lagged model $y_t ...
2
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0answers
24 views

Holt-Winters vs. comparison to history

I have a timeseries with daily and weekly seasonality that I want to check for anomalies (on data as it comes in live). I could use Holt-Winters forecasting, or I could just compare the data with the ...
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6 views

Forecasting Adjusted Time Based Cohort Values Based on Variance

I'm trying to forecast the distribution of sales for a three week cohort with adjustments for the remaining weeks made from the past weeks results. The basic approach would be to adjust the next weeks ...
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0answers
17 views

How to compare three univariate time-series?

I'm new at the StackExchange but got a lot of useful info by reading the posts in the past. Now I'm struggling to find the best way to statistically address my research question and would love to get ...
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0answers
15 views

First difference of non-stationary - does the prediction accumulate the errors?

I am modeling a non-stationary process (I(1) actually), it looks like this: I have 146 data points (monthly data). The ideal model in my case should have: Macro-variables sensitivity Predict the ...
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7 views

Transform Variable R [migrated]

I have a data frame as shown below: ...
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15 views

How to code dummy variables for structural breaks in VAR

This question is really 2-in-1: 1) How do I code dummy variables for the following series that has 2 structural breaks in trend; an initial upward trend, then a much flatter upward trend, then ...
3
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1answer
35 views

Variance of a multivariate AR(1) process

I have a multivariate AR(1) process (first-order vector autoregression, VAR(1)) of the form $$ \pmb X_{t+1} = A \pmb X_t + \zeta_t $$ where $\pmb X_t$ is a vector, $A$ is a matrix and $\zeta_t \sim ...
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0answers
14 views

Calculating change in PC scores using PCA for paired temporal data

I'm trying to calculate change in scores on a depression questionnaire - a very simple problem. However, what I care about is not the change in raw score, but rather the change in principal component ...
0
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0answers
29 views

Effect of Federal Funds Rate on SP500

I have been trying to examine the effect of the Federal Funds Rate on the S&P 500 using regression analysis. Obviously via the graph its easy to see this is a hard relationship to define over a ...
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0answers
20 views

Different AIC values for the same SARIMA model in different R packages

I've been trying to run a simple time series model for a data set. My biggest question is when I run the auto.arima model (from package "forecast) for this data set ...
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2answers
18 views

Holt-Winters for Imputation

I have found Holt-Winters seasonal method a very decent method for forecast, specifically for cases where more recent observations are more representative of the near future. The method equally sounds ...
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0answers
54 views

Procedure of clustering seasonality in several time series in R

I want to do cluster analysis of a product monthly sales during 5 years in 30 stores (my data are time series). I want to cluster the stores according to its seasonality. This is an example of my ...
0
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1answer
116 views

How to graph throughput

UPDATE 2: maybe a simple way to state it: for web application performance data consisting on: start timestamp | end timestamp | response time I'd like to compute a non-aggregated data set consisting ...
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0answers
8 views

what should take frequency for Daily Summarizes Transaction data.

I am little confuse about to take a frequency for time series data on daily transaction data in ts() function. I have 2 years of Daily Transaction data (Means How many amount withdrawal in a day ...
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22 views

Out-of-sample forecasting with time series data

I have 54 data points. my objective is to (1) decompose my data into trend and cycles and (2) to forecast. My question is that as I only have 54 data points is it necessary for me to HOLD some sample ...
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0answers
21 views

AUC (and other measures) dependent on the way data is split

I am applying machine learning (XGBoost) to certain problem regarding time series classification, as input as uses some numerical values around 200 features and vectorized text (tfidf). The result I ...
2
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60 views

Pros and cons of time series models [closed]

Can anybody help me to figure out the pros and cons of time series models(AR, MA, ARMA, ARIMA, ARIMAX, ARCH, GARCH etc.)? I'm confused what models I should choose in what kind of circumstances. Thanks ...
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55 views

Predicting multivariate uneven time series of discrete/categorical data

I have a basic background in stats, DSP, ML etc. but by no means an expert so some of my terminology is going to be rusty. It probably makes the most sense if I simply show you what i wanted to do and ...
0
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0answers
79 views

Principal components analysis: relationship between first and second principal component

I'm really struggling with understanding the idea of Principal Component Analysis and would appreciate any help. We have a m multivariate input time series $ \begin{align} X_{t} &= ...
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23 views

Could neural network models be used to model a data-set containing 4 inputs -> time, current, temperature, state of charge and 1 output -> Voltage

I have a battery measurements data-set containing 4 inputs -> time, current, temperature, state of charge and output -> Voltage Although I have mentioned time, the time values vary in sets of ...
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5 views

CausalImpact and choosing the start of effect time-frame

Is it probable, to experimentally choose a prior starting point to the factual starting point of a n effect in order to validate the package's results? I guess the point gets more clear if you look ...
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18 views

How does Causalimpact work? (please see more specific questions in the description)

How does CausalImpact behave when the number of data points in the time-series is unequal to n times the set length of a season (for example when there are 30 data points with the length of the ...
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21 views

Identify jumps in electricity prices

Have a time series that is a function of normally distributed variable and a non normal distributed variable. Now if I want to identify the normal distributed part, how would one proceed? I have ...
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32 views

Out-of-Sample forecasting with ARIMA

I am trying to forecast crude oil price using Arima. I am using in-sample data to build the Arima model and am using out-of-sample data to test if the model can generalize well to other data. I am ...
5
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3answers
110 views

Interpolating/smoothing 8-bit data

(As a caveat, I think this belongs on this stack site, but I'm not 100% sure.) We have a time series that is physically sampled with only 8bit resolution, so we wind up with a "staircase" pattern, ...
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1answer
30 views

Measure of Central Tendancy in ARIMA?

I know very little about the ARIMA - the AutoRegressive Integrated Moving Average - model but I am interested in what type of central tendency is represented by "average". Is it simply the mean? If ...
0
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32 views

Classification of multivariate time series datasets

I have data where each feature is a multivariate time series dataset with a known class label. Each feature is of dimension 4xn and contains per-second measurements of 4 different variables A, B, C ...
0
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1answer
111 views

How to visualize data averaged over ranks?

I have data from twelve experiments, with seven days and five animals on each. Based on the seven day mean of each animal's data, it is a assigned a rank from 1 to 5. I would like to combine the data ...
5
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1answer
62 views

Is it valid to use an ARMAX model for TV Attribution?

Suppose I have a website which has some baseline hourly traffic. I also run TV advertising intermittently which drives up my web traffic. I want to determine how much effect my TV advertising is ...
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9 views

Image convolution vs signal convolution

What are the similarity and differences between image convolution (e.g., convolution for filtering) and time signal convolution? Apparently I am confused about the formulation of them. Thanks!
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0answers
15 views

Help in understanding an image clustering technique described in a paper

Paper titled :Mammographic images segmentation based on chaotic map clustering algorithm DOwnload link presents a technique of image clustering using chaotic map. I explain briefly : A chaotic map is ...
4
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1answer
45 views

How to compute estimate for the first time series value using ARIMA model?

I modeled a univariate time series in R using the Arima command. One can obtain fitted values for the original series using this command by applying the function ...
0
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0answers
19 views

Using low frequency data to calibrate high frequency data

I have a 10 Hz time series measured by a fast instrument and a 1 minute time series measured by a slow reference instrument. The data consists of a fluctuating meteorological parameter. The slow ...
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0answers
13 views

augmented dickey fuller statistic

I am trying to write code for an ADF test in C++. As I understand it, the Dickey-Fuller statistic is a modified version of the t-statistic. Currently, I have functioning code that calculates a t score ...