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

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Simple algorithm for online outlier detection of a generic time series

I am working with a large amount of time series. These time series are basically network measurements coming every 10 minutes, and some of them are periodic (i.e. the bandwidth), while some other ...
10
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2answers
3k views

How to find a good fit for semi-sinusoidal model in R?

I want to assume that the sea surface temperature of the Baltic Sea is the same year after year, and then describe that with a function / linear model. The idea I had was to just input year as a ...
23
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5answers
3k views

Efficient online linear regression

I'm analysing some data where I would like to perform ordinary linear regression, however this is not possible as I am dealing with an on-line setting with a continuous stream of input data (which ...
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5answers
3k views

Seeking certain type of ARIMA explanation

This may be hard to find, but I'd like to read a well-explained ARIMA example that uses minimal math extends the discussion beyond building a model into using that model to forecast specific cases ...
22
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10answers
10k views

Books for self-studying time series analysis?

I started by Time Series Analysis by Hamilton, but I am lost hopelessly. This book is really too theoretical for me to learn by myself. Does anybody have a recommendation for a textbook on time ...
15
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4answers
4k views

Period detection of a generic time series

This post is the continuation of another post related to a generic method for outlier detection in time series. Basically, at this point I'm interested in a robust way to discover the ...
19
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2answers
4k views

Proper way of using recurrent neural network for time series analysis

Recurrent neural networks differ from "regular" ones by the fact that they have a "memory" layer. Due to this layer, recurrent NN's are supposed to be useful in time series modelling. However, I'm not ...
25
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3answers
4k views

Is it possible to do time-series clustering based on curve shape?

I have sales data for a series of outlets, and want to categorise them based on the shape of their curves over time. The data looks roughly like this (but obviously isn't random, and has some missing ...
6
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2answers
16k views

How to statistically compare two time series?

I have two time series, shown in the plot below: The plot is showing the full detail of both time series, but I can easily reduce it to just the coincident observations if needed. My question is: ...
6
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3answers
916 views

What to make of explanatories in time series?

Having worked mostly with cross sectional data so far and very very recently browsing, scanning stumbling through a bunch of introductory time series literature I wonder what which role explanatory ...
2
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4answers
1k views

Moving-average model error terms

This is a basic question on Box-Jenkins MA models. As I understand, an MA model is basically a linear regression of time-series values $Y$ against previous error terms $e_t,..., e_{t-n}$. That is, the ...
4
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2answers
351 views
10
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3answers
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How to fit an ARIMAX-model with R?

I have four different time series of hourly measurements: The heat consumption inside a house The temperature outside the house The solar radiation The wind speed I want to be able to predict the ...
21
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4answers
1k views

Data has two trends; how to extract independent trendlines?

I have a set of data that is not ordered in any particular way but when plotted clearly has two distinct trends. A simple linear regression would not really be adequate here because of the clear ...
5
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1answer
3k views

How to correlate two time series with gaps and different time bases?

I asked this question over on StackOverflow, and was recommended to ask it here. I have two time series of 3D accelerometer data that have different time bases (clocks started at different times, ...
24
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5answers
8k views

Time series 'clustering' in R

I have a set of time series data. Each series covers the same period, although the actual dates in each time series may not all 'line up' exactly. That is to say, if the Time series were to be read ...
15
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9answers
3k views

Time series for count data, with counts < 20

I recently started working for a tuberculosis clinic. We meet periodically to discuss the number of TB cases we're currently treating, the number of tests administered, etc. I'd like to start ...
11
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5answers
13k views

What method can be used to detect seasonality in data?

I want to detect seasonality in data that I receive. There are some methods that I have found like the seasonal subseries plot and the autocorrelation plot but the thing is I don't understand how to ...
11
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6answers
2k views

How to detect a significant change in time series data due to a “policy” change?

I hope this is the right place to post this, I considered posting it on skeptics, but I figure they'd just say the study was statistically wrong. I'm curious about the flip side of the question which ...
11
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2answers
2k views

Getting started with neural networks for forecasting

I need some resources to get started on using neural networks for time series forecasting. I am wary of implementing some paper and then finding out that they have greatly over stated the potential of ...
4
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3answers
504 views

Forecasting time series based on a behavior of other one

Apologies for this vague and unclear question, I have no background in statistics. I have two vectors of time series data, covering a six month period. The data is in daily intervals (except for ...
10
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1answer
662 views

How do I incorporate an innovative outlier at observation 48 in my ARIMA model?

I am working on a data set. After using some model identification techniques, I came out with an ARIMA(0,2,1) model. I used the detectIO function in the package ...
3
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2answers
4k views

How to compare two time series?

I have collected temperature readings for 4 locations on a beach in Cornwall UK using data loggers. The loggers recorded temperature every 15 mins accurate to .1 of a degree (Celsius). The loggers ...
1
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2answers
936 views

How do I write a mathematical equation for ARIMA (0,2,1) x (0,0,1) period 12

I would appreciate if someone could help me write the mathematical equation for the seasonal ARIMA (0,2,1) x (0,0,1) period 12. I'm a little confused with how to go about this. I would prefer an ...
10
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2answers
441 views

Why are MA(q) time series models called “moving averages”?

When I read "moving average" in relation to a time series, I think something like $\frac{(x_{t-1} + x_{t-2} + x_{t-3})}3$, or perhaps a weighted average like $0.5x_{t-1} + 0.3x_{t-2} + 0.2x_{t-3}$. ...
11
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3answers
2k views

Good introductions to time series (with R)

I am currently collecting data for an experiment into psychosocial characteristics associated with the experience of pain. As part of this, I am collecting GSR and BP measurements electronically from ...
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2answers
2k views

Computing correlation (and the significance of said correlation) between a pair of time series

I have two time series S, and T. they have the same frequency and the same length. I would like to calculate (using R), the correlation between this pair (i.e. S and T), and also be able to calculate ...
8
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1answer
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Time series clustering

I have many time series in this format 1 column in which I have date (d/m/yr) format and many columns that represent different time series like here: ...
5
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1answer
3k views

How to setup xreg argument in auto.arima() in R? [closed]

I am working on a small project with one time series which measures the customer visit data (daily). My covariates are a continuous variable Day to measure how many ...
5
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3answers
2k views

Outliers spotting in time series analysis, should I pre-process data or not?

My question builds on a previous post on outlier detection in generic time series, and specifically on the answer provided by the always great Rob H. I work for a small-sized manufacturing company ...
7
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2answers
2k views

Predicting daily electricity load - fitting time series

I want to predict inter-day electricity load. My data are electricity loads for 11 months, sampled in 30 minute intervals. I also got the weather-specific data from a meteorological station ...
6
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2answers
7k views

Auto.arima with daily data: how to capture seasonality/periodicity?

I am fitting an ARIMA model on a daily time series. Data are collected daily from 02-01-2010 to 30-07-2011 and are about newspaper sales. Since a weekly pattern in sales can be found (the daily ...
4
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2answers
2k views

Correlating volume timeseries

Consider the following graph: The red line (left axis) describes the trading volume of a certain stock. The blue line (right axis) describes the twitter message volume for that stock. For instance, ...
14
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2answers
3k views

Testing significance of peaks in spectral density

We sometimes use spectral density plot to analyze periodicity in time series. Normally we analyze the plot by visual inspection and then try to draw a conclusion about the periodicity. But has the ...
11
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5answers
10k views

How many lags to use in the Ljung-Box test of a time series?

After an ARMA model is fit to a time series, it is common to check the residuals via the Ljung-Box portmanteau test (among other tests). The Ljung-Box test returns a p value. It has a parameter, h, ...
4
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3answers
255 views

When is it appropriate to select models by minimising the AIC?

It is well established, at least among statisticians of some higher calibre, that models with the values of the AIC statistic within a certain threshold of the minimum value should be considered as ...
11
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1answer
299 views

Updating classification probability in logistic regression through time

I am building a predictive model that forecasts a student's probability of success at the end of a term. I’m specifically interested in whether the student succeeds or fails, where success is usually ...
8
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2answers
745 views

Box-Jenkins model selection

The Box-Jenkins model selection procedure in time series analysis begins by looking at the autocorrelation and partial autocorrelation functions of the series. These plots can suggest the appropriate ...
7
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3answers
439 views

What is the term for a time series regression having more than one predictor?

It's pretty tough to search the Web for info on something when you don't know what words are commonly used to describe it. In this case, I'm wondering what it's called when you include another ...
4
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2answers
2k views

Non-Correlated errors from Generalized Least Square model (GLS)

As a financial institution, we often run into analysis of time series data. A lot of times we end up doing regression using time series variables. As this happens, we often encounter residuals with ...
4
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2answers
845 views

Outlier detection for generic time series

In this case, "generic" being the entire gauntlet of macroeconomic time-series that private and government statistical offices put out. Some background - I recently started working at a data provider ...
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1answer
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3
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475 views

How to assess effect of intervention in one state versus another using annual case fatality rate?

I am a beginner in statistics with just basic knowledge. I have these data: cases, deaths and CFR (Case Fatality Rate-deaths per 100 cases) of a disease for 17 years (1994-2010) from 2 neighbouring ...
9
votes
4answers
335 views

Interpolation of influenza data that conserves weekly mean

Edit I have found a paper describing exactly the procedure I need. The only difference is that the paper interpolates monthly mean data to daily, while preserving the monthly means. I have trouble to ...
4
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3answers
2k views

How to identify transfer functions in a time series regression forecasting model?

I am trying to build a time series regression forecasting model for an outcome variable, in dollar amount, in terms of other predictors/input variables and autocorrelated errors. This kind of model ...
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3answers
13k views

How do you interpret results from unit root tests?

I have to do some unit root tests for a project, I'm just unsure on how to interpret the data (which is what I have been asked to do). Here is one of my results: ...
5
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1answer
228 views

Conditional Expectation has the minimum mean prediction error

Consider $\{X_t\}$ as a general time series including random variables $X_t$. Assume that we have observed $X$'s until time t. The goal is to come up with a function of the observed $X$'s to predict a ...
2
votes
1answer
511 views

AR(1) coefficient is correlation?

Is the ar1 coefficient from an AR(1) model the "first order correlation of the noise" of a time series? I'm using R's aws package and one of the arguments of the ...
18
votes
1answer
9k views

How to apply Neural Network to time series forecasting?

I'm new to machine learning, and I have been trying to figure out how to apply neural network to time series forecasting. I have found resource related to my query, but I seem to still be a bit lost. ...
16
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3answers
2k views

What are disadvantages of state-space models and Kalman Filter for time-series modelling?

Given all good properties of state-space models and KF, I wonder - what are disadvantages of state-space modelling and using Kalman Filter (or EKF, UKF or particle filter) for estimation? Over let's ...