Data Science Stack Exchange
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Questions tagged [time-series]

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

Overview

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

Time series analysis includes:

  • Trend identification (e.g. autocorrelation)
  • Temporal pattern recognition
  • Spectral analysis / wavelet analysis
  • Forecasting (e.g. regression)
  • Anomaly detection

The salient characteristic of methods of time series analysis (as opposed to more general methods to analyze relationships among data) is accounting for the possibility of serial correlation (also known as temporal correlation) among the data. Positive serial correlation means successive observations in time tend to be close to one another, whereas negative serial correlation means successive observations tend to oscillate between extremes. Time series analysis also differs from analyses of more general stochastic processes by focusing on the inherent direction of time, creating a potential asymmetry between past and future.

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Time series prediction using ARIMA vs LSTM

The problem that I am dealing with is predicting time series values. I am looking at one time series at a time and based on for example 15% of the input data, I would like to predict its future values. So far I have come across two models: LSTM…
ahajib
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Adding Features To Time Series Model LSTM

have been reading up a bit on LSTM's and their use for time series and its been interesting but difficult at the same time. One thing I have had difficulties with understanding is the approach to adding additional features to what is already a list…
Rjay155
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Machine learning - features engineering from date/time data

What are the common/best practices to handle time data for machine learning application? For example, if in data set there is a column with timestamp of event, such as "2014-05-05", how you can extract useful features from this column if any? Thanks…
Igor Bobriakov
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Time Series prediction using LSTMs: Importance of making time series stationary

In this link on Stationarity and differencing, it has been mentioned that models like ARIMA require a stationarized time series for forecasting as it's statistical properties like mean, variance, autocorrelation etc are constant over time. Since…
Abhijay Ghildyal
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How to deal with time series which change in seasonality or other patterns?

Background I'm working on a time series data set of energy meter readings. The length of the series varies by meter - for some I have several years, others only a few months, etc. Many display significant seasonality, and often multiple layers -…
Jo Douglass
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Looking for a good package for anomaly detection in time series

Is there a comprehensive open source package (preferably in python or R) that can be used for anomaly detection in time series? There is a one class SVM package in scikit-learn but it is not for the time series data. I’m looking for more…
pythinker
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Convert a pandas column of int to timestamp datatype

I have a dataframe that among other things, contains a column of the number of milliseconds passed since 1970-1-1. I need to convert this column of ints to timestamp data, so I can then ultimately convert it to a column of datetime data by adding…
Austin Capobianco
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How can I predict traffic based on previous time series data?

If I have a retail store and have a way to measure how many people enter my store every minute, and timestamp that data, how can I predict future foot traffic? I have looked into machine learning algorithms, but I'm not sure which one to use. In…
user1132959
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Python library to implement Hidden Markov Models

What stable Python library can I use to implement Hidden Markov Models? I need it to be reasonably well documented, because I've never really used this model before. Alternatively, is there a more direct approach to performing a time-series analysis…
yad
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Prediction interval around LSTM time series forecast

Is there a method to calculate the prediction interval (probability distribution) around a time series forecast from an LSTM (or other recurrent) neural network? Say, for example, I am predicting 10 samples into the future (t+1 to t+10), based on…
4Oh4
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Modelling Unevenly Spaced Time Series

I have a continuous variable, sampled over a period of a year at irregular intervals. Some days have more than one observation per hour, while other periods have nothing for days. This makes it particularly difficult to detect patterns in the time…
doublebyte
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RNN using multiple time series

I am trying to create a neural network using time series as input, in order to train it based on the type of each series. I read that using RNNs you can split the input into batches and use every point of the time series into individual neurons and…
Ploo
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How to train model to predict events 30 minutes prior, from multi-dimensionnal timeseries

Experts in my field are capable of predicting the likelyhood an event (binary spike in yellow) 30 minutes before it occurs. Frequency here is 1 sec, this view represents a few hours worth of data, i have circled in black where "malicious" pattern…
William D
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Recognize a grammar in a sequence of fuzzy tokens

I have text documents which contain mainly lists of Items. Each Item is a group of several token from different types: FirstName, LastName, BirthDate, PhoneNumber, City, Occupation, etc. A token is a group of words. Items can lie on several…
OoDeLally
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Classify Customers based on 2 features AND a Time series of events

I need help on what should be my next step in an algorithm I am designing. Due to NDAs, I can't disclose much, but I'll try to be generic and understandable. Basically, after several steps in the algorithms, I have this: For each customer that I…
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