Questions tagged [time-series]

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

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64
votes
5answers
41k views

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 ...
60
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10answers
62k views

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 ...
11
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1answer
18k views

Keras LSTM with 1D time series

I'm learning how to use Keras and I've had reasonable success with my labelled dataset using the examples on Chollet's Deep Learning for Python. The data set is ~1000 Time Series with length 3125 with ...
25
votes
2answers
11k views

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 ...
14
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2answers
8k views

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 ...
3
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2answers
182 views

Time-series multi-step generalization from single step model

I have built a generic stacked lstm model of the form: ...
85
votes
5answers
56k views

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....
21
votes
4answers
20k views

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 ...
17
votes
5answers
15k views

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 ...
4
votes
2answers
13k views

How to classify and cluster this time series data [duplicate]

I have post already the question few months ago about my project that I'm starting to work on. This post can be see here: Human activity recognition using smartphone data set problem Now, I know ...
8
votes
1answer
4k views

Is time series multi-step ahead forecasting a sequence to sequence problem?

I'm using the keras package in order to train an LSTM for a univariate time series of type numeric (float). Performing a 1-step ahead forecast is trivial, but I'm not sure how to perform a, let's say, ...
8
votes
2answers
6k views

DTW (Dynamic Time Warping) requires prior normalization?

I'm trying DTW from mlpy, to check similarity between time series. Should I normalize the series before processing them with DTW? Or is it somewhat tolerant and I can use the series as they are? All ...
8
votes
1answer
2k views

Binary classification of every time series step based on past and future values

I'm currently facing a Machine Learning problem and I've reached a point where I need some help to proceed. I have various time series of positional (x, ...
5
votes
2answers
5k views

RNN time-series predictions with multiple features containing non-numeric features and numeric features?

The question RNN's with multiple features is ambiguous and not explicitly in differentiating different features. I want to understand how to use RNN to predict time-series with multiple features ...
5
votes
1answer
4k views

Anomaly detection for transaction data

I have transaction details for credit data (bank transfers, peer to peer transfers, etc). Currently, I have one year worth of data which I cannot properly classify. I'm looking for input and ...
5
votes
5answers
31k views

Additive vs Multiplicative model in Time Series Data

The above time series plot is a daily closing stock index of a company. I want to know which model between additive and multiplicative best suits the above data. I know what the two models are, but i ...
1
vote
2answers
3k views

What are the best ways to use a time series data for binary classification

I have large number of csv files and each of them are timeseries based csv files sampled at Avery 5 seconds for 2-3 mins. I have 20k such files with 200-300 variables in each file. I am aggregating ...
8
votes
1answer
3k views

Using time series data from a sensor for ML

I have the following data for a little side project. It's from an accelerometer sitting on top of a washer/dryer and I'd like it to tell me when the machine has finished. x is the input data (x/y/z ...
3
votes
2answers
357 views

Time series binary classificaiton with labelling issues

My situation is quite complicated so I will give a similar example from a simpler domain. Suppose we want to try to predict WHEN a mobile game users will make a purchase if given a sale. Almost every ...
29
votes
1answer
11k views

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, ...
7
votes
3answers
22k views

Anomaly detection on time series

I've just started working on an anomaly detection development in Python. My data sets are a collection of timeseries. More in details, data are coming from some sensors/meters which record and ...
17
votes
4answers
14k views

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 ...
11
votes
3answers
18k views

What is the best method for classification of time series data? Should I use LSTM or a different method?

I am trying to classify raw accelerometer data x,y,z to its corresponding label. What is the best architecture for best results? Or, does anyone have any suggestions on LSTM architectures built on ...
4
votes
1answer
901 views

Train LSTM model with multiple time series

I am predicting energy usage for a bedroom within a school residential building with date, temperature, and humidity as input features, using 7 time-steps and ...
8
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5answers
2k views

Time-series grouped cross-validation

I have data with the following structure: ...
5
votes
3answers
885 views

Quasi-categorical variables - any ideas?

Let's say I'm trying to predict a person's electricity consumption, using the time of day as a predictor (hours 00-23), and further assume I have a hefty but finite amount of historical measurements. ...
6
votes
1answer
3k views

Similarity measure for multivariate time series with heterogeous length and content

I am interested in clustering multivariate N time series of T'values' each(different lengths) using python. Each variable have many trends and values which are simultaneously numeric and nominal. A ...
4
votes
1answer
961 views

RNNs for time series prediction - what configurations would make sense

My question here is mostly about general-intuition logic: when using a RNN (LSTM) for predicting a time series, and you have the goal of, for example, predicting at ...
15
votes
3answers
5k views

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 ...
10
votes
2answers
5k views

Forecasting non-negative sparse time-series data

I have a time-series dataset (daily frequency) representing the sales of a product to a customer over time. The sales is represented as the following: $$[0, 0, 0, 0, 24, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, ...
8
votes
2answers
189 views

Time-series prediction: Model & data assumptions in AI/ML models vs conventional models

I was wondering if there was a good paper out there that informs about model and data assumptions in AI/ML approaches. For example, if you look at Time Series Modelling (Estimation or Prediction) ...
6
votes
3answers
13k views

How can Time Series Analysis be done with Categorical Variables

Most of the time series analysis tutorials/textbooks I've read about, be they for univariate or multivariate time series data, usually deal with continuous numerical variables. I currently have a ...
6
votes
2answers
2k views

Recurring events - finding in a time series

I have an event dataset from which I would like to detect recurring events (i.e: weekly, bi-weekly, monthly). The dataset contains: Timestamp (date) Event type which can get any value (e.g: ...
5
votes
2answers
7k views

Time series forecasting with RNN(stateful LSTM) produces constant values

I have a time series daily data for about 6 years(1.8k data points). I am trying to forecast the next t+30 values, Train data independent matrix (X)=Sequences of previous 30 day values Train (Y)=The ...
8
votes
6answers
20k views

Check similarity between time series

I have time series of parameters A, B, C and D. All of ...
8
votes
6answers
3k views

Generate timeseries data

Training would be bad if training data is not sufficient. Techniques like SMOTE or ADASYN can be used for oversampling. For image data, we can blur or change the angle to generate more samples from ...
8
votes
2answers
5k views

Is it valid to shuffle time-series data for a prediction task?

I have a time-series dataset that records some participants' daily features from wearable sensors and their daily mood status. The goal is to use one day's daily features and predict the next day's ...
7
votes
2answers
7k views

How to classify movement data (time series) in real time

I have some movement data sampled over a time series. I am trying to classify the movements in real time as either smooth or shaky. For example, as the movement is smooth it is classified as smooth ...
5
votes
1answer
6k views

Is there an R tutorial of using LSTM for multivariate time series forecasting?

There is a great blog post about how to use keras stateful LSTM in R to forecast sunspots. I applied it to financial ts data ...
2
votes
0answers
964 views

Recommended model for univariate or multivariate multistep ahead time series forecasting

I have a dataset consisting of recurring and non-recurring expense transactions from bank accounts, as well as other features describing the bank account and each transation. I aggregate these ...
5
votes
2answers
3k views

Tool for labeling audio

I have few thousand audio signals to label into 2 different classes and save them to numpy array for further training of models. MATLAB recently released ...
4
votes
3answers
2k views

Is time series forecasting possible with a transformer?

For my bachelor project I've been tasked with making a transformer that can forecast time series data, specifically powergrid data. I need to take a univariate time series of length N, that can then ...
4
votes
2answers
247 views

When forecasting time series, how does one incorporate the test data back into the model after training?

When you build a classification or regression model, you typically split the data into a train data set and a test data set. The test data is a randomly selected subset of the overall data. Once you ...
3
votes
1answer
1k views

Multivariate Time-Series forecasting using LSTM

I have a dataset of hourly measures of pollution('Sample_Measurement) and weather condition. If I want to predict the pollution level of the current hour using the weather and pollution data of the ...
3
votes
2answers
1k views

Are RNN or LSTM appropriate Neural Networks approaches for multivariate time-series regression?

Dear Data Science community, For a small project, I've started working on Neural networks as a regression tool, but I am still confused about possibilities of some variants. Here's what I am aiming ...
2
votes
1answer
2k views

LSTM Time series prediction for multiple multivariate series

I have to predict next min traffic for multiple cities (100+). I am thinking of using LSTM. My main concern is how do I scale the number of cities. How does LSTM learn different amount of traffic and ...
2
votes
1answer
558 views

Binary classification model with time series as variables

This is probably a simple question. Assume I'm interested in modelling a binary variable, with various covariates, including ones that are time series observations. In the usual modelling approach, ...
1
vote
1answer
2k views

Python Time series: extracting features on a rolling window basis

I have a long univariate time series, and before performing some machine learning models with it, I want to extract as many features as I can from the time series on a rolling-window basis. As a ...
1
vote
1answer
2k views

Anomaly detection in Time Series Data - Help Required [closed]

I am looking for algorithms on Anomaly detection for time series data. It is uni-variate analysis, considering single parameter (inlet pressure) of air compressor sensor data. The objective is to ...
1
vote
2answers
1k views

Pros and cons of pandas or R for longitudinal data?

Note: I believe this question is not off-topic because it meets all of the criteria for subjective questions that are allowed. I would be happy to rephrase or clarify if others disagree I'm about to ...