Questions tagged [time-series]

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

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84
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....
64
votes
5answers
40k 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
votes
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 ...
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, ...
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 ...
21
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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 ...
19
votes
7answers
12k views

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 ...
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 ...
17
votes
1answer
83k views

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 ...
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 ...
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 ...
14
votes
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 ...
14
votes
1answer
8k views

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 ...
14
votes
1answer
276 views

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 ...
13
votes
1answer
10k views

Can Reinforcement learning be applied for time series forecasting?

Can Reinforcement learning be applied for time series forecasting?
13
votes
3answers
14k views

How can autoencoders be used for clustering?

Suppose I have a set of time-domain signals with absolutely no labels. I want to cluster them in 2 or 3 classes. Autoencoders are unsupervised networks that learn to compress the inputs. So given an ...
13
votes
1answer
654 views

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 ...
12
votes
3answers
4k views

How to animate growth of a social network?

I am seeking for a library/tool to visualize how social network changes when new nodes/edges are added to it. One of the existing solutions is SoNIA: Social Network Image Animator. It let's you make ...
12
votes
5answers
12k views

How to merge monthly, daily and weekly data?

Google Trends returns weekly data so I have to find a way to merge them with my daily/monthly data. What I have done so far is to break each serie into daily data, for exemple: from: 2013-03-03 - ...
12
votes
2answers
16k views

When to use Stateful LSTM?

I'm trying to use LSTM on time-series data in order to generate future sequences that looks like the original sequences in term of values and progression direction. My approach is: train RNN to ...
11
votes
3answers
19k views

Dynamic Time Warping is outdated?

At http://www.speech.zone/exercises/dtw-in-python/ it says Although it's not really used anymore, Dynamic Time Warping (DTW) is a nice introduction to the key concept of Dynamic Programming. I ...
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 ...
11
votes
4answers
4k views

Feature Extraction Technique - Summarizing a Sequence of Data

I often am building a model (classification or regression) where I have some predictor variables that are sequences and I have been trying to find technique recommendations for summarizing them in the ...
11
votes
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 ...
11
votes
2answers
5k views

Trying to use TensorFlow to predict financial time series data

I'm new to ML and TensorFlow (I started about a few hours ago), and I'm trying to use it to predict the next few data points in a time series. I'm taking my input and doing this with 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, ...
9
votes
3answers
30k views

How to remove outliers using box-plot?

I have data of a metric grouped date wise. I have plotted the data, now, how do I remove the values outside the range of the boxplot (outliers)? All the ['AVG'] data is in a single column, I need it ...
9
votes
4answers
11k views

Classify multivariate time series

I have a set of data composed of time series (8 points) with about 40 dimensions (so each time series is 8 by 40). The corresponding ouput (the possible outcomes for the categories ) is eitheir 0 or 1....
9
votes
2answers
3k views

LSTM Feature selection process

We need to implement a time series problem with the LSTM model. But, while implementing the same, the main challenge I am facing is the feature selection issue. Because our data-set contains 2300 ...
9
votes
2answers
1k views

Input for LSTM for financial time series directional prediction

I'm working on using an LSTM to predict the direction of the market for the next day. My question concerns the input for the LSTM. My data is a financial time series $x_1 \ldots x_t$ where each $x_i$...
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
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
5answers
2k views

Time-series grouped cross-validation

I have data with the following structure: ...
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, ...
8
votes
2answers
177 views

Linearly increasing data with manual reset

I have a linearly increasing time series dataset of a sensor, with value ranges between 50 and 150. I've implemented a Simple Linear Regression algorithm to fit a regression line on such data, and I'm ...
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 ...
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 ...
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
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
1answer
8k views

How fbprophet cross validation works

I am facing some issues to understand how cross_validation function works in fbprophet packages. I have a time series of 68 days (only business days) grouped by 15min and a certain metric : 00:00 ...
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) ...
8
votes
2answers
4k views

LSTM: How to deal with nonstationarity when predicting a time series

I want to do one-step-ahead predictions for time series with LSTM. To understand the algorithm, I built myself a toy example: A simple autocorrelated process. ...
8
votes
1answer
5k views

how to compare different sets of time series data

I am trying to do some anomaly detection between time#series using Python and sklearn (but other package suggestions are definitely welcome!). I have a set of 10 time-series; each time-series ...
8
votes
2answers
2k views

Forecasting Multiple (few hundreds) uni-variate time series with inflated zeros

I am a novice seeking help to gain experience in Data Science. Let us take a scenario where a big company would like to forecast its sales (a specific product) across different stores in different ...
7
votes
2answers
5k views

Why are RNN/LSTM preferred in time series analysis and not other NN?

I had recently a great discussion about the advantages of RNN/LSTM in time series analysis in comparison to other Neural Networks like MLP or CNN. The other side said, that: The NN just have to be ...
7
votes
1answer
2k views

Finding unpredictability or uncertainty in a time series

I am interested in finding a statistic that tracks the unpredictability of a time series. For simplicity sake, assume that each value in the time series is either 1 or 0. So for example, the following ...
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 ...
7
votes
1answer
7k views

Multivariate Time Series Binary Classification

I have continuous (time series) data. This data is multivariate. Each feature can be represented as time series (they are all calculated on a daily basis). Here is an example. ...
7
votes
3answers
17k views

Multivariate Time-Series Clustering

I have a streaming data along with timestamp dataset that looks like this: 1.png Timestamp can be inclusive of "seconds" too, but the data may or may not change every second. it depends on ...
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 ...

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