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Questions tagged [time-series]

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

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69 votes
5 answers
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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 ...
Rjay155's user avatar
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0 votes
2 answers
162 views

What is the state-of-the-art in prediction\classification missing labels in partially labeled data?

Overview Let's say I have the following data: ...
Mario's user avatar
  • 400
63 votes
10 answers
67k 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 ...
Igor Bobriakov's user avatar
27 votes
2 answers
12k 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 ...
Jo Douglass's user avatar
12 votes
1 answer
21k 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 ...
user1147964's user avatar
5 votes
2 answers
935 views

Time-series multi-step generalization from single step model

I have built a generic stacked lstm model of the form: ...
Fra's user avatar
  • 101
14 votes
2 answers
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 ...
William D's user avatar
  • 143
86 votes
8 answers
67k 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....
ahajib's user avatar
  • 1,075
24 votes
4 answers
25k 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 ...
pythinker's user avatar
  • 1,267
20 votes
5 answers
17k 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 ...
neural-nut's user avatar
  • 1,783
9 votes
1 answer
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, ...
sevelf's user avatar
  • 91
9 votes
1 answer
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, ...
Chris's user avatar
  • 245
8 votes
2 answers
9k 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 ...
KcFnMi's user avatar
  • 343
8 votes
1 answer
4k 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 ...
laktak's user avatar
  • 181
6 votes
3 answers
5k 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 ...
Alexey Abramov's user avatar
5 votes
1 answer
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 ...
Kira's user avatar
  • 51
5 votes
2 answers
7k 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 ...
hhh's user avatar
  • 171
5 votes
5 answers
52k 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 ...
Jor_El's user avatar
  • 231
4 votes
2 answers
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 ...
Jakubee's user avatar
  • 401
3 votes
2 answers
489 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 ...
Keith's user avatar
  • 326
2 votes
2 answers
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 ...
Anurag Upadhyaya's user avatar
0 votes
1 answer
521 views

What is the best\correct data split approach over time-series data to compare performance of forecasting future data among ML and DL regressors?

Let's say I have dataset contains a timestamp (non-standard timestamp column without datetime format) as a single feature and count as Label/target to predict ...
Mario's user avatar
  • 400
0 votes
1 answer
231 views

multi dimensional time series and matrix profile method

I have a time series of the following format: ...
user18602524's user avatar
37 votes
1 answer
16k 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, ...
PixelPioneer's user avatar
17 votes
5 answers
17k 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 ...
4Oh4's user avatar
  • 308
15 votes
3 answers
7k 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 ...
doublebyte's user avatar
12 votes
6 answers
33k views

Check similarity between time series

I have time series of parameters A, B, C and D. All of ...
KcFnMi's user avatar
  • 343
11 votes
4 answers
29k 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 ...
Brian Yen's user avatar
  • 111
11 votes
3 answers
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 ...
rosy's user avatar
  • 143
10 votes
2 answers
11k 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 ...
Han's user avatar
  • 103
10 votes
2 answers
6k 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, ...
Bernardo Aflalo's user avatar
10 votes
5 answers
4k views

Time-series grouped cross-validation

I have data with the following structure: ...
David Masip's user avatar
  • 6,101
8 votes
2 answers
245 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) ...
Maeaex1's user avatar
  • 550
7 votes
2 answers
8k 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 ...
Matt Findlay's user avatar
7 votes
1 answer
4k 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 ...
user23440's user avatar
7 votes
3 answers
7k 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 ...
Sebastian Eliassen's user avatar
7 votes
6 answers
5k 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 ...
vipin bansal's user avatar
  • 1,272
7 votes
4 answers
24k 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 ...
Giordano's user avatar
  • 345
6 votes
2 answers
3k 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: ...
DeQ's user avatar
  • 61
5 votes
3 answers
1k 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. ...
Uri Merhav's user avatar
4 votes
1 answer
1k 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 ...
sunday's user avatar
  • 41
4 votes
1 answer
3k 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 ...
maggs's user avatar
  • 345
4 votes
1 answer
149 views

How to know if a time series sequence is predictibale or just random (Univariate time series prediction)?

I'm trying to predict a current value of a variable based on the its previous 10 values. I tried multiple time series approaches including ARIMA, LSTM and linear regression... None of them really ...
the phoenix's user avatar
4 votes
1 answer
1k 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 ...
NeuronQ's user avatar
  • 93
4 votes
1 answer
7k views

How to find similar time series?

I've got a collection of yearly data (one value per year per category), and I'd like to find series that are most similar to one another. Example data is here. I don't know much about data science, ...
user2315852's user avatar
4 votes
2 answers
722 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 ...
Alex S Kinman's user avatar
3 votes
1 answer
3k views

How to predict NaN (missing values) of a dataframe using ARIMA in Python?

I have a dataframe df_train of shape (11808, 1) that looks as follows: ...
some_programmer's user avatar
3 votes
2 answers
28k views

Time series forecast using SVM?

I have a pandas data frame like this: ...
vizakshat's user avatar
  • 465
3 votes
2 answers
146 views

How bootstrapping works for prediction intervals?

I'm experimenting with prediction interval (PI) over univariant time-data using skforecast pythonic package.. in the documentation it is mentioned that: Prediction intervals A prediction interval ...
Mario's user avatar
  • 400
3 votes
2 answers
5k views

VAR model ValueError: x already contains a constant

I'm using VAR model for multivariate time series. The structure is that although each variable is a linear function of past lags of itself and past lags of the other variables, one and/or two of the ...
Abs 's user avatar
  • 181