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

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How to combine time-series based features with different frequencies

I have 3 features which I want to use in my classifier. They are all time-series data-based. However, they are all at different frequencies and there have different matrix dimensions. I was wondering ...
2
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0answers
7 views

ACF and PACF plot analysis

I am new to ARIMA, and I am trying to understand these lag plots. Are the following ACF and PACF suggesting that the lag of my time series is 4? If I am wrong, please help me understand these plots. ...
5
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2answers
450 views

Python module for change point analysis

I'm looking for a Python module that performs a change-point analysis on a time-series. There are a number of different algorithms and I'd like to explore the efficacy of some of them without having ...
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25 views
+50

R: Fitting a model with periodic, nonlinear and categorical components

Can anyone give me some advice on how to fit a model with linear (some categorical), non-linear and time series components in R? I don't want to use a non-parametric model like a Loess smooth or ...
8
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1answer
754 views

Assessing peaks in time series of cell signal data

I am measuring for the existence of response in cell signal measurements. What I did was first apply a smoothing algorithm (Hanning) to the time series of data, then detect peaks. What I get is this: ...
0
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1answer
51 views

How to forecast time-series bounded by [0,1], i.e., forecast relative frequencies?

I am working with time series values which are all in the closed interval [0, 1]; these values represent relative frequencies, i.e., empirical probabilities. I would like to create a model such that ...
2
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2answers
43 views

Capturing Seasonality in Multiple Regression for daily data

I have a daily sales data for a product which is highly seasonal. I want to capture the seasonality in the regression model. How I can do it? I have read that if you have quarterly or monthly data, in ...
2
votes
1answer
150 views

Forecasting model inputs that are both auto-correlated and are calibrated over time?

How does one account for model inputs that are both a) auto-correlated and b) calibrated over time? I'm interested in forecasting the outcomes of sporting events. Let's say that each team has a score ...
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0answers
13 views

Arima model - multi step forecast

The following code shows a forecast of the next 24 hours of my electricity prices with two exogenous variables. My problem is, that I don't know how to build a forecast for the next 3 days or more ...
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1answer
55 views

SV model estimation in R using tsbugs

I have been trying to estimate the basic stochastic volatility model using OpenBUGS via R and at an stage of the following command. Please can you comment for the ...
0
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0answers
13 views

Holt Winters Initialization Issue

I am using an additive seasonal Holt-Winters model to compute confidence band of my data. I followed the HW initialization process described by Rob J Hyn­d­man. The confidence band is derived by ...
0
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3answers
273 views

Time lag between correlated signals

What options are there for finding out what the time lag is for different time series? I'm looking at market data here - for example, if sugar does bad in a year, it's likely that soda might be hit ...
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0answers
11 views

Two or more time series. What is the best way to test whether one of them is leading and by what time period?

I am trying to prove/disprove that one time series is leading trend for the other ones. Two time series are (probably) independent and the movements are caused by some (let's assume unknown) common ...
2
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2answers
5k views

How to normalize two time series for comparison?

I have two time series a and b, which I want to compare. Due to their range difference I normalize them first. a(i), b(i) are natural numbers for i=1,...,N two ...
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0answers
8 views

References suggested for multivariate analysis of several similar time series

I have a time series dataset that reports the hourly page views and social media shares of online news stories. What I hope to obtain is the relationship between the two variables. I would imagine ...
1
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1answer
56 views

Increasing the accuracy of tbats() forecasts by factoring for correlations between different time-series?

This question builds on my previous question Forecasting Hourly Time Series based on previous weeks and same period in previous year/s. My project is to forecast the number of ~400 different types of ...
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0answers
8 views

Searching for time series inside another time series

I have a time series "A" and another one "B". I would like to find occurrences of "B" inside "A". Typically, "A" is much bigger (magnitude: millions of points) than "B" (magnitude: hundreds of points) ...
7
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5answers
189 views

If two time series $X$ and $Z$ follow $0 \leq Z \leq X$, can we say that $\text{var}(Z) \leq \text{var}(X)$?

Now I see it can't hold. Thank you for the counter examples... You guys rule! Thank you very much for your comments! I added, however, some observations that were missing. Most importantly is the ...
4
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1answer
124 views

Chart for seeing trends, correlations, triggers and patterns

I've scraped a set of data, and I was wandering what the best way to start analyzing it was. My initial thoughts were to use a multi y axis graph, like this: The reason I was thinking about a ...
0
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1answer
224 views

Testing threshold cointegration in vector error-correction models

In Hansen and Seo's paper on Testing two regime threshold cointegration in VECM (J. Econometrics, 2002; 110:293), the authors proposed a test based on Lagrange Multiplier for testing treshold in ...
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0answers
22 views

Is this the proper way to create a simple linear time series model in R

I'm trying to create a simple ols model over time from a time series. Here's what I have cagr.lm.time <- lm(cagr.xts ~ time(cagr.xts)) Where cagr.xts is the ...
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1answer
12 views

Quantifying Change in a Histogram Valued Timeseries

I'm attempting to do binary classification where my raw features are collections of histograms that are recorded in a time series. These histograms are scaled to sum to 1. To be more precise and ...
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21 views

Evaluating the temporal corelation model for which the differencing is performed for trend removing

According to the literature, for temporal correlation modeling the trend should be removed from the time-series data. We choose differencing for removing the trend. I would like to know: When we ...
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1answer
20 views

MAPE is high for daily sale prediction

I have daily sales data from 2011 to 2013. I have to do prediction for 2014.I have used arima and exponential method to predict the daily sale, but it is not giving the better result. MAPE is around ...
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5 views

Standard deviation of multiple root mean square values

I am calculating the R.M.S. of a periodic signal that has a stochastic component to it. For every period I am able to calculate a value for R.M.S. using the following function: $R.M.S. = ...
2
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1answer
167 views

Time-varying Coefficients

I have time series data on fish catches from 1950-2011. I wish to implement a regression model with varying coefficients. I'm aware that cox models etc. exist and implementation via the ...
5
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1answer
204 views

Is there a statistical measure for how much a variable fluctuates over time?

Is there a statistical measure for how much a variable fluctuates over time? For example a noise signal fluctuates a lot. However, if you would sort all values of the signal in time, you would have a ...
4
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1answer
128 views

What is the implication of unit root of MA?

A ARMA(p,q) process is weakly stationary, iff the root of its AR part is not on the unit circle. So its weak stationarity doesn't depend on its MA part. But what can the positions of the roots of its ...
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0answers
14 views

Mann-Kendall test STATA

I am new in this forum. I am beginning to work with time series, I have a daily (25,000+ observations) temperature dataset (01/01/1946 - 07/01/2014) I want to test for the following: Trends: So ...
0
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1answer
59 views

How can i find the significance of the cointegrating coefficients in output cajorls-function in R?

I investigate the long-term relationship of some variables but in the output provided by cajorls-function, I can't see for each coefficient if it is significant? This is provided by the ...
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0answers
21 views

Forecasting time series with missing data and irregular intervals

I have a data set of medical drug stock levels at health centres and I want to forecast monthly consumption over the following 3-6 months. However about 30%-40% of the data is missing and some of the ...
1
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1answer
120 views

Trying to use Holt-Winters to fit this data

I'm trying to fit the data in this message (daily temperatures) using the Holt–Winters technique in R, but can't get the seasonal example in here to work. Is this not possible with these data, or am I ...
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1answer
16 views

Getting expected value of future value with time varying data (credit card revolving and fee data) . Customer lifetime value

I have a credit card data and that contains monthly amount of revolving and amount of fee for each customer. As a bank perspective, I want to get the expected value of future revolving amount and fee ...
2
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1answer
52 views

find the point at which the curve significantly shoots up

so this is getting a little complex for me and hope someone can help me out. I do not have a mathematical background. I have a time series of daily rainfall for 50 years for a particular location. ...
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0answers
36 views

Calculate the average of hourly data of three sensors

I am trying to calculate the average of hourly data of three sensors but the hourly timestamps of all three sensors are different. How is it possible to measure the average of hourly data of all three ...
3
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2answers
171 views

Regression of data that includes a date

I have a dataset that contains a few hundred transactions from a three suppliers operating in 100+ countries over a three year period. We've found that the country of sales is not a significant ...
0
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4answers
197 views

Most effective way to learn time series with poor quant background

End goal will be practical application (model building) by using time series analysis to analyze/forecast macroeconomic/finance data. Background: I have taken stats, introductory econometrics, ...
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2answers
69 views

Best way to test for co-occurrence of measures

I have some data with temporal measures over time. I'd like to test whether two binary variables co-occur more often than chance would predict, and I'm wondering the best (simple) way to do that. The ...
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1answer
166 views

How to map a trajectory to a vector?

I have a series of data points in this form (timestamp, lat, long) for a set of users. Each user has a trajectory when he travels from point A to point B. There might be any number of points from A to ...
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12 views

Are there any time-series visualization tools available for HDF5 files?

I have gigabytes of time-series data stored in HDF5 files and while there is the very basic visualization tool available in HDFViewer, I'm interested in knowing if there are rich visualization ...
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22 views
+50

Interval prediction with variable rates of change

I mainly need help figuring out what statistics I need to apply to my data, because it's a pretty strange problem: Crypto-currencies base their understanding of time on the number of blocks. A block ...
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0answers
33 views

How to perform proper data mining on time-series data?

I have some daily data from city A, B, C. Values from city A are highly correlated with values from other cities for lag -1,-2,-3 and -4. I want to use Random Forest, SVM and ANN to predict values ...
2
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1answer
18 views

Can I difference after fitting a time series regression model?

Suppose that I have a time series that exhibits a notable trend, and I want to test a hypothesis that a second variable is related to that trend. I fit a linear regression model with that second ...
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1answer
32 views

Time Series Data Mining Library?

Can anyone recommend a library for time series data mining tasks other than predictive modeling and statistical analysis? There seem to be a number for these purposes (e.g., Gretl), but nothing for ...
5
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1answer
50 views

When forecasting sequential data is it best to use auto-regressive models or build a more traditional n x p dataset with features?

I'm familiar with the use of auto-regressive models when it comes to forecasting a single vector of time-series data. Is anybody familiar with a more traditional modeling approach, i.e. - creating ...
0
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1answer
60 views

Error Calculating MVN Likelihood of Time Series with AR(1) Errors in R

I'm having trouble calculating the likelihood of a time series with AR(1) errors. I am generating my covariance matrix according to page 2 of (http://cran.r-project.org/doc/contri...regression.pdf), ...
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23 views

ets() or stlf()

Every where I read, experts suggested to use ets() to better determine alpha, beta, gamma ...
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1answer
34 views

How to compare difference between two time series?

I am working on my thesis where I'm examining how strong emotion people show to different events. My problem is (1) that I have VERY little experience with statistics and math, so I'm kind of lost ...
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1answer
79 views

How to sum correlations, or, calculate correlation of disjointed variables

I'm trying to calculate the correlation of two variables, but the array is disjointed in the middle - but I'm trying to obtain one correlation coefficient. See the excel file I uploaded. Because ...
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0answers
29 views

Forecasting agricultural commodity prices with R

I would like to create a predictive model in order to forecast the price of an agricultural raw material. I got time series for the prices and the production of this raw material, and also for the ...