Linked Questions

68
votes
9answers
74k views

What algorithm should I use to detect anomalies on time-series?

Background I'm working in Network Operations Center, we monitor computer systems and their performance. One of the key metrics to monitor is a number of visitors\customers currently connected to our ...
18
votes
5answers
23k views

Detecting changes in time series (R example)

I would like to detect changes in time series data, which usually has the same shape. So far I've worked with the changepoint package for R and the ...
13
votes
2answers
10k views

Timeseries analysis procedure and methods using R

I am working on a small project where we are trying to predict the prices of commodities (Oil, Aluminium, Tin, etc.) for the next 6 months. I have 12 such variables to predict and I have data from Apr,...
13
votes
5answers
48k views

How do I detrend time series?

How do I detrend time series? Is it ok to just take first difference and run a Dickey Fuller test, and if it is stationary we are good? I also found online that I can detrend the time series by ...
9
votes
2answers
12k views

How to use auto.arima to impute missing values

I have a zoo series with many missing values. I read that auto.arima can impute these missing values? Can anyone can teach me how to do it? thanks a lot! This is ...
9
votes
3answers
5k views

Transfer function in forecasting models - interpretation

I am occupied with ARIMA modelling augmented with exogenous variables for promotional modelling purposes and i have hard time explaining it to business users. In some cases software packages end up ...
5
votes
3answers
6k views

Intervention Analysis Coding in R TSA Package

I am studying intervention analysis in time series with the Cryer and Chan book and am looking at trying to understand how to code the step response interventions. One question I had is how to ...
4
votes
2answers
4k views

Dealing with spikes in data

A company sells chocolates. Demand is recorded weekly. The future demand is estimated using the sales for every week in the previous 3 years. But the sales pattern is corrupted by promotions that have ...
2
votes
4answers
1k views

How to detect abnormality in an otherwise very systematic and regular time-series data for temperature measurement?

I have time-series data, let's say a pandas series, with time (sampling frequency is hourly) as its index and temperature measurement across that time. I want some statistical/time-series principle ...
1
vote
1answer
636 views

What are some useful robust and scalable approaches towards anomaly detection of a time series data?

What are some useful robust and scalable approaches/methods towards anomaly detection of a time series data? I am mainly looking for some practical approaches carried out using Python, R, Java, etc. ...
1
vote
0answers
197 views

Using ARIMA with exogenous regressors for outlier detection in R

I would like to detect outliers in real-time data that is aggregated per hour. For this example, I've selected the hourly pedestrian data from Melbourne, Australia (Pedestrian volume (updated monthly),...
1
vote
0answers
118 views

Forecasting in R and accounting for historical events (such as product price changes)

I'm trying to use the Holt-Winters model included with the R package 'forecast' to forecast a product's sales revenues, which includes seasonality. In the past the product's price as changed, and ...