Questions tagged [time-series]

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

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38 views

Time series forecast for everyday for till a distant future

I have time series data for every single day from last 5 years with seasonal variation and a general increase in trend. This is what my data looks like: And I am trying to predict for every single ...
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23 views

Unsynchronized time series visualization

I would like to visualize a large amount of events composed of time serie windows. A typical event would be: Problem is, my events are not synchronized, and so if I plot them all, it would look like: ...
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Advice on imputing temperature data with StatsModels MICE

This may be a dumb question but I can't figure out how to actually get the values imputed using StatsModels MICE back into my data. I have a dataframe (dfLocal) with hourly temperature records for ...
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How to train a deep neural network with time-series images and unbalanced dataset?

I have images that represent a fixed-length time-window of different serials. Serials have time-series of different size, so e.g. serial1 has length 30, serial2 length 110 and so on. I have multiple ...
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1answer
55 views

What Models should i try for this problem?

I need some advice for a problem i'm working on with automobile data. The vehicles provide a series of codes at every second which are bieng stored, though it can vary how many. For example , at time ...
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1answer
31 views

Is feature scaling at all needed for a feature set with a single feature?

I understand that feature scaling is required to bring features in different magnitudes on a common scale so the model is not biased towards features with higher magnitudes. But if there is only a ...
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Is it mandatory to have a DateTime index for a pandas dataframe to run ARIMA models on it?

I'm trying to learn about Arima models. In every tutorial, the Dataframe considered has a DateTime index. When I looked upon the mathematical formula for Arima model, it only depends on the past ...
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34 views

YOLO for timeseries, Error when training

I'm currently trying to implement the YOLO object detection algorithm for the localization and classification of events in time series ('signals'). To do this, I have defined a custom loss function, a ...
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1answer
24 views

Why is shuffling timeseries a bad thing?

I'm trying to understand precisely why it is a bad idea to shuffle time-series when splitting train and test data. Like, what is false about shuffling time-series? How does it tamper with the model?
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41 views

Timeseries LSTM: does test data need to come after training data?

I have one single, very long time series. I want to train an LSTM to distinguish between two behaviours (A or B) at every timestep (sequence-to-sequence). Because the time series is very long, I plan ...
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1answer
114 views

Clustering sequences of sentence embeddings

I have a sequence of events, right now I am not worried about their actual times, just the order. This is a sequence of web page views. I have modelled my data as the following, where each element ...
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1answer
350 views

ML methods for prediction, using categorical variables and time

Most of the time series analysis tutorials/textbooks I found time series data, usually deal with continuous numerical variables. I am currently trying to solve a problem that deals with multivariate ...
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How to modify a Convolutional Neural Network architecture built for a univariate time series to multivariate time series?

I have built a CNN (in combination with a LSTM cell) that takes 1D time series-like data as an input and performs classification. I am obtaining a good performance, but the complete data has actually ...
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Does a 3-D input shape (time series with features) to an LSTM Model Evaluate the Label at each Time Step?

I have a problem similar to the one posed in this video: https://www.youtube.com/watch?v=flMCYqIn3eg In that video, she had a set of data on a number of individual debtors and needed to find out if ...
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5 views

Using forecasting values of wind speed at different hours in the future to predict power output at different hours in the future for a wind turbine

I need to design a neural network model for a wind turbine which takes as input the forcasting values of wind speed as follows : windspeedPlus001hr , windspeedPlus002hr , windspeedPlus003hr , ...
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Can LSTM be used to predict value as regression problem?

I have time-series data as shown below. Which model is generally preferred if grig_id is needed to be predicted? Is it possible to use LSTM with a sigmoid ...
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1answer
133 views

Reinforcement Learning on real time data over a web server

Question: is it possible to implement a reinforcement learning model over a NodeJS server? This server would be receiving binary forms of data (open /close; yes/no) in real time. The objective for ...
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26 views

Time-Series analysis with small data set, but long sequences

I'm working on a time-series classification problem. There are 3 classes. Dataset consists of 6 sequences from each class (total 18 sequences). Each sequence is 19,000 in length. What are some time-...
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1answer
44 views

Understanding time series anomaly detection using Autoencoder

I'm studying how to detect anomalies in the time series using an Autoeconder. In particular, I'm following the guide posted in the Keras website, but I don't understand why they are creating and how ...
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1answer
24 views

Should the times series be made stationary before doing a clustering analysis?

For times series analysis and forecasting, we try to make the times series stationary before proceeding with the experiment. I would like to know if such a procedure is necessary if one is working on ...
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1answer
38 views

which Model to apply on panel data where unique id has 6-8 records and total records are 2,000,000?

I am new to such panel data where I have multiple observation for same ID in different Quarter and I am not sure what kind of machine learning algorithm I can apply. I have data from Q1-18 till Q4-...
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1answer
33 views

Sensorfusion: Generate virtual sensor based on analysis of sensorsdata

I have a steam engine which is equipped with the following sensors: temperature sensor in the boiler room temperature sensor in the heating room pressure sensor in the boiler room rotations-per-...
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1answer
20 views

How to compute time-lagged correlation between two variables with many examples at each time t?

I have a dictionary of following form: datetimes = {year : {name : (score1, score2)}} #there are 50+ names/year So, essentially, I'm trying to get an aggregate ...
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1answer
62 views

How to treat patients without events in time-to-event analysis?

I'm working with longitudinal data for a series of patients. Duration of followup on a patient-level is non-uniform. Patients can either experience a discrete event (e.g., a heart attack) or never ...
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1answer
39 views

Create a single time series plot of multiple devices [closed]

I have a dataset where there are stored the measurements of 30 devices. Each device has about 4000 values and it is structured as well: ...
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What models should I try with a time series database? [closed]

I've acquired and cleaned a dataset that shows statistics from every county in New York State during 2010-2019 focusing on the NYS School Aid correlating it to other growth and criminal statistics. ...
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How meaningful are the results when you difference the time series dataset before clustering?

On a certain task where I need to perform K-Means Time Series Clustering with DTW algorithm, I would like to know how credible the results are when performing clustering on the original vs a dataset ...
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1answer
232 views

Voting classifier using grid search for Time Series

I have three models: Arima Auto ARIMA Double Exponential Smoothing I would like to apply an ensemble method - a voting method and allow the classifier to learn weights for these three models. I ...
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1answer
23 views

what indicators can be used to classify a group of stocks?

I am working with a dataset contains the daily return time series of 50 different stocks, I want to divide these stocks into several different groups. My idea was to make a new dataset contains some ...
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2answers
2k views

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

Hello Practitioners, Being a newbie seeking help to gain experience in Data Science. Lets take a scenario where a big company wants to forecast its sales (a specific product) across different stores ...
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1answer
25 views

Finding an appropriate binary classification algorithm for time series data intervals

Maybe someone here has experience in this matter and can point me in the right direction. I want to classify parts of an interval of numerical movement data as either resting or not resting. I have ...
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1answer
36 views

Plot overlapping time series [closed]

I'm trying to plot my test set and test set predictions to check the differences and see how my autoencoder reconstructed the data, but since I have a test set 30x10 I have a huge visualization ...
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1answer
22 views

How to interpret the original time series approximated using principal component analysis? [closed]

I've read some posts about PCA applied on time series, but still a bit confused and I have the following questions(Suppose I am working with a time series of the return of 50 industries and I want to ...
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14 views

Finding optimal time series using convolution [closed]

we logged sensor data while milling a workpiece. At several points, the workpiece was damaged and this induced a certain sensor data time series. Due to noise and since its a real world measurement, ...
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1answer
13 views

Architectures that take inputs of mixed sampling rates

Let's say a model is trained on multiple datasets of 1D time series. These datasets have been gathered with different sampling rates. I plan to use a convolution neural network to process these time ...
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1answer
49 views

time series prediction using arima and non linear trend and too much residuals

I am working on forecasting a financial index, i tried decomposing the time series using : ...
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0answers
15 views

estimating single or multiple model for Multiple Time Series Forecasting

I am a newbie in the ML field. So please, neglect or better correct, if I am wrong somewhere. I am working on a requirement where details of loading time for each page/component will be given. Now I ...
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1answer
38 views

preprocessing time sequence

I have a long list of event (400 unique events, sequence ~10M long). I want to train an RNN to predict next event. The preprocessing steps i took are: (1) turning to OneHotEncoding using pandas: <...
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1answer
4k views

Monthly trend with fb prophet-Interpreting the graph

I have monthly data with month/year in one column and price on another. I would like to get a yearly trend with fb prophet library in python (how to use monthly data with the library is explained at ...
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0answers
5 views

Selecting time window for very different feature lengths

I'm doing my first steps with python, keras and a 1D time series classification. I can train a model, save it, apply it to validation or test data. I'll build my first implementation around this ...
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3answers
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Why are predictions from my LSTM Neural Network lagging behind true values?

I am running an LSTM neural network in R using the keras package, in an attempt to do time series prediction of Bitcoin. The issue I'm running into is that while my predicted values seem to be ...
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0answers
17 views

PyTorch LSTM with varying time steps

Is it possible to create an LSTM in PyTorch where the time steps are varying? For example, heights where measurements are taken at various times. The data might look like this: Person id Inches tall ...
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0answers
40 views

Train/validation/test and cross-validation on panel dataset

(Cross-posting a previous question from CrossValidated in case it is more suitable here: Train/Validation/Test and Cross-Validation on Panel Dataset ) I have a panel dataset, indexed by $Year$ and $...
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17 views

AI with metadata and timeseries inputs

In data science you sometimes encounter a scenario where you have meta data on a given process and the process data itself. For example you have a mechanical component that is tested over time. So you ...
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1answer
51 views

Predicting when component will fail having its parameters data

I have a component and I need to predict when it will wear out and will need replacement. I monitor, let's say 5 parameters of this component, each one is monitored for every run cycle. So, the ...
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1answer
20 views

Predict the first observations of a time series when order of the model is higher

Suppose you have you have a time series with 365 observations, one for each day of the year, and you split the first 183 rows in training set and the latest 182 in test set. Suppose you create an AR (...
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3answers
22k 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 ...
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5answers
1k views
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1answer
65 views

How to measure/rate the effect of a exogenous covariate in a ARIMAX Model?

I have an ARIMA model, I'm trying to figure out how much an external variable (exogenous covariate) could improve the forecast, so I need to "synthesize" a rate that tell me the usefulness (or impact) ...
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1answer
28 views

How to use id's in binary classification problem

I would like to predict for a given user (on a website) if he/she logs out from the website within ten minutes. In terms of data, I have a user ID and timestamp of the latest post on the website. ...

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