Questions tagged [time-series]

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

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Finding negative drop in time series data

I'm trying to detect the negative peaks in this time-series data. There are two series of data (catch: they fall at the same time). The problem I'm facing is that the peak is relative to the ...
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What model should I use to predict a time series like this?

This series is calculated from the difference of two day's stock index. I rescaled it using sklearn's StarndardScaler. It seems LSTM does not work well on this series.
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Timeseries data analysis

Data is as below, use code to create a larger sample. ...
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Feature engineering of timestamp for time series analysis

Following a Tensorflow time series analysis tutorial, I came across a particular way of converting data timestamps into a time-of-day periodic signal, that could help the model interpret the data ...
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Workflow for stock prediction in machine learning

I'm trying to find the best workflow for a stock prediction problem. My idea goes as follows : I will use a classfication and a regression at the same time Classification (-1 ; 0 ; 1) Regression (...
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How to interpret these ACF & PACF and get SARIMA parameters?

First time doing time series for my startup. I found tons of information on the web including in Cross Validated but most are contradicting with each other and none of them seem to apply to my ACF and ...
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Applicability of ARIMA model on non stationary data

I have a time series dataset that does not have the stationary property. The dataset is monotonically increasing or sometimes showing no change over periods of time. Can I apply the ARIMA model to ...
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Explanation of spectral residual algorithm for outlier detection

I've been reading the paper https://arxiv.org/pdf/1906.03821.pdf for spectral residual outlier detection, but I don't quite understand it. Specifically, in the implementation there are three variables ...
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Is it possible to reduce the time of computing DTW with dtw-python package by disabling computation of?

I am trying to classify some time series using dtw-python package which is a python version of R package implementing Dynamic Time Warping described in this nice ...
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Time Series Classification of Google Search

I am a newbie to the Time series Classification, I have data like this, ...
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Why we call Mix-up method is a data augmentation technique?

I am bit confused in the Mixup data augmentation technique, let me explain the problem briefly: What is Mixup For further detail you may refer to original paper . We double or quadruple the data ...
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Detecting Data Drift in Audio Data

For a give set of audio files collected from an industrial process via a microphone, I have extracted suitable features and fed them into a neural network for training a binary classifier as depicted ...
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Can t-SNE be applied to visualize time series datasets

I have multiple time-series datasets containing 9 IMU sensor features. Suppose I use the sliding window method to split all these data into samples with the sequence length of 100, i.e. the dimension ...
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Seasonal random walk formula, Forecasting Principles and Practice

Seasonal naïve method: A similar method is useful for highly seasonal data. In this case, we set each forecast to be equal to the last observed value from the same season of the year (e.g., the same ...
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Forecasting for multiple (Irregular spaced) Timeseires with interdependence

I have already asked a simmilar question, but i thoguth that this was not phrased well and hence i am trying a new post were i ask a better question. Let me know if this is ok. Judging by some of the ...
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How to estimate time interval with external time-dependent regressors?

I'm on a team that is tackling a project similar to the following. Suppose you want to estimate the age of a plant using a small set of tabular data features. In addition to the plant data, you have ...
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How to detect whether an entire series is an outlier relative to others?

I have multiple price series of the same asset as follows. Visually, it is obvious that series "A" (the flat line) is an outlier, and series "E" (the line with the zig-zag pattern)...
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How to deal with temporal trend in ML

I am fitting a binary classifier and I observe a temporal trend in the response variable, meaning that the actual percentage of positives fluctuates with time, I can see periods where it is high and ...
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Methods to extract / merge signal from three noisy time series (of same event)

I have three time series of same length, all containing magnitude measurements of the same event "A". But each time series is using a different method of measurement. My goal is to merge the ...
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Multiple time series forecasting method

Your help will be very useful in the below exercise as I have issues in my try to identify the correct ML approach to solve it. My target here is to predict the value of the of 23:00 hour for the last ...
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Fail to decompose and make stationary time series

I am looking for some suggestions for my time series. I am dealing with the column "Temperature (C)" from this dataset. I am trying to make it stationary in order to do some forecasting on ...
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Why does Bahdanau Attention Have to be Causal?

Using the Bahdanau attention layer on Tensorflow for time series prediction, although conceptually it is similar to NLP applications. This is how the minimal example code for a single layer looks like....
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Should I normalise or detrend time series data before creating MLP models

Am building MLP models on forecasting timeseries data. Am new in the field of machine learning and I have read about Detrending and normalisation. So which method (normalisation or detrending) will be ...
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1answer
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How to evaluate goodness of fit of a sinusoidal model using an F-Test?

I have 2000 samples of unevenly sampled timeseries data collected at 2 hour intervals over the course of 24 hours. Working in Python, I used scipy.optimize.curve_fit...
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Time series classification for multiple unique IDs

I need to predict if a user will buy the product or not using the journey of the user in the website. I have a datetime column and so one user will have more than one row because it is tracking every ...
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Time Series Hyperparameter Tuning

My question is about the intuition for hyperparameter tuning of time series. In other models, like Linear or Logistic Regression there is labeled data and according to accuracy or precision, the ...
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Price Predition for Irregular spaced historic data of non independent Prices

I am a little unsure how to proceed. I am not an expert but on a decent intermediate level when it comes to regular Timeseries. Now i am faced with a problem that first seemed related, but is an ...
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Persistence and stationarity together

I am trying to analyse a time series. I want to get only quantitative results (so, I'm excluding things like "looking at this plot we can note..." or "as you can see in the chart ...&...
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Is my dataset a time series dataset? and should I use an LSTM

I have a dataset where I am recording temperature after every 4milliseconds till 500 and another feature 'conductivity value'. The length of the dataset is around a 1000 rows. I need to find the ...
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23 views

Appropriate Supervised Machine Learning Algorithm for Time series prediction

I am looking forward to the correct ML/algorithm approach for the below issue. My target here is to predict the target day of the incoming time series below for a new time series. Also below you can ...
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Applying SMOTE on time series data

I have a dataset that consist of student grades and it's based on a time series. I used LSTM to predict the student future grade. Can I apply SMOTE on this dataset to ensure that the model will not be ...
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What are my options for classifying multiple multivariate time series and making live predictions?

I'm looking for some ideas on how to handle a multivariate time series classification problem. Specifically, I'm dealing with many hundreds of time series of varying length (but within the same ...
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How does Kalman filtering work backwards?

For time series missing value imputation I am using the Kalman filter/smoothing approach, given in the imputeTS package. As Kalman filter is iterative and needs a view data points to make its estimate ...
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1answer
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panda grouping dates by variable with transposed value variables

I had read this post panda grouping by month with transpose and it gave me the nearest answer to my question but not the completely solution. How would I get somewhat like the reverse output? My ...
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When an Fourier Analysis should be used for timeseries data

When Fourier Analysis should be used for time-series data,except when doing decomposition?
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Automatize autocorrelation in python

I'm trying to automatize my autocorrelation study in Python. My question is: is it possible? Let me explain. I have a time series and I just learnt how to interpret the autocorrelation plot. My ...
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Fractional Differencing/Differentiation for Non-Time based Model; Look-ahead bias?

I have time-series data, but instead of using a time-based model like RNN, I've decided to approach my classification problem using an lgbm classifier. To do so, I have modified the data, such that ...
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1answer
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Anomaly detection and replacing it with past values in time series

I am trying to use anomaly detection to find the anomalies in my time series, and if I find it, I will replace it with my past values. I'm trying to do this because I want to create an upper and lower ...
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Cross-validation with time series data and Deep Learning Models

I have in a pandas dataframe two columns: "GDP" and "Unemployment Rate", from 1950 until now. I'm trying to apply a simple RNN model to this data, where we would use past and ...
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Understanding plotted ACF on stationary time-series data

I made time-series data stationary and plotter ACF & PCF, while PCF looks fine, I do not know how to interpretate ACF, as it looks like this - I could not say it geometrical. For PACF - it is ...
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Random kernels in multivariate Rocket sktime

Does anyone know for when Rocket is applied in the multivariate setting how random kernels are generated? Namely is a 1-D kernel randomly generated and applied to a randomly selected feature? Or is a ...
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LSTM with variable time steps

I'm reading this post that describes how to train LSTMs with variable time step lengths. But does that have repercussions? Should I preprocess the time series in to varying permutations? e.g. Should ...
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1answer
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How to handle multi time series data for 10K + items

There are 50 shops and each shop have 30000 items. Goal is to forecast the sale of item based on shop. Forecase the item_cnt_day, for this i dont see this as multi ...
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Two-sided grubbs test KeyError: 355

I am trying to this two sided grubbs test by passing in a pandas.Series object and an appropriate alpha value. whenever I do the test on the whole dataset, I have ...
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How to combine data from multiple Google Trends queries effectively?

As you might know, Google Trends works by normalising a random sample of the search term data, with the sample changing at least once per day, from my experience. This is not an issue for western ...
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Looking for an algorithm to perform classification on multivariate grouped time series

I will be grateful for any help. I have multivariate time series, where every one of them has an unique ID. Also, there is a variable giving information about the trend type of the ID from a point of ...
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23 views

LSTM behaviour with return_sequences and TimeDistributed

I am trying different models for a classification problem with sequence data and variable sequence length, the below model predict all at once, and it achieve better results than other models, so I ...
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Suggestions for binary time-series-classification model for small dataset

Hopefully I´m at the right place for my question: I´m looking for suggestions for models to use to classify multivariate time series. I´m trying to find a way of classifying the behaviour of motors ...
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1answer
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PACF for Airline Passengers dataset: What's wrong?

The airline passengers dataset is available here, but it also comes with in R. I'm working with python, and I import the following (besides the usual like pandas and numpy.) ...

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