Questions tagged [time-series]

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

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7 views

Removing the effect of Time series X on time series Y, when their relation is unknown

I am working on a dataset of 6 years measurements of a water quality parameter called 'chla' ( parameter 'X') measured by a sensor for each year from May to October. The parameter has its own trend ...
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Is the forward chaining CV really suitable for time series?

If the time series distribution is non-stationary, we have to retrain our model once in a while (because it must forget the old dependencies and learn new ones). In forward chaining CV we use longer ...
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Data leakage in bidirectional LSTM timeseries data

Does it cause data leakage to train a bidirectional LSTM on data where a user can be a sample in the training data multiple times? Each row is a snapshot at a different point in time for a given ...
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Anomaly Detection Problem (Time Series)

I have given an anomaly detection problem as follows. I want to find the anomaly score in my dataset given that it has 8 features(coming from 8 different sensors). What are the steps and best ...
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Time Series prediction using R

I have a dataset which contains data related to the exchange rate in a certain time period(2013-2015). The dataset has a column date with YYYY/MM/DD format and USD/EUR which contains the exchange rate....
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SPC vs Autoencoders in anomaly detection

Considering the usage of Autoencoders in anomaly detection of time-series data, why SPCs (control charts) have lost their charm? Are there any advantages with Autoencoders and disadvantages with SPCs?
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LSTM: many to one and many to many in time-series prediction

I am trying to predict the trajectory of an object over time using LSTM. I have three different configurations of training and predicting values in my mind and I would like to know what the best ...
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Time series regression

I m new in the domain of machine learning. I m here to ask for some elucidation. I have a data set presented as a time series( from a strain sensor coming from a wind turbine). In this time series, ...
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How to deal with discrete variables in Multivariable Time Series forecasting?

I am tackling this time series forecasting problem to basically predict number of sales in the future training dataset looks like this: ...
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What is the best approach for send time optimization? [closed]

I could no find a lot information about how the companies doabout send time optimization, either for push notifications or email campaigs. having historical data about clicks and sends what would be ...
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Comparison of Time series models

How to compare two different forecasting models lets say one is the classical statistics-based model and the other is a machine learning-based. Also, let's say if I have to forecast the unit volume ...
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Is data leakage in time series due to both I's of the IID principe or only one?

I am sure that the Independent part of the IID principle gives you data leakage because of the correlation. But the identical part I am not so sure. Identical in time series means that your data is ...
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Multivariate Time-Series Analysis where features are dependent with each other [closed]

I have time series problem in which I have to forecast sales of 1000s of products on a weekly basis. The sales of these products depends on many factors, such as in which location they are selling and ...
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Forecasting sales during time of epidemic

As we are going through a tough time because of the Coronavirus epidemic, is it possible to somehow include this affect of this in predicting sales as a time-series for next few weeks? I am new to ...
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Time Series Anomaly Detection [closed]

I have been given an anomaly detection problem as follows. I want to find the anomaly score in my dataset given that it has 8 features (coming from 8 different sensors). What are the steps and best ...
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14 views

KNN with non-stationary time series data

Given features (X,Y,Z) where X is the target feature, and a matrix of (X,Y,Z) data points ordered chronologically: GOAL: At time i, a prediction for Xi must be generated from the available ...
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What model do I use to predict a regression problem with timeseries data

Overall Goal To predict how much reagent "A" I started with in a reaction. Data: To predict this I have timeseries data of reagent "B". For each time step a measurement of reagent "B" is taken (the ...
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specifics of dilated convolutions in tensorflow

This ones a little esoteric. I'm developing a wave-net inspired model. First time needing dilated convolutions. My question is which values is the convolution looking at relative to the output ...
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How to impute missing values for hour of day?

I have a dataset containing a certain amount of bookings, no shows and cancellations. We assume that a cancellation less than 2 days is the same as a no show (since you did not have the time to ...
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How to identify recurring patterns in this set of transactional data

I'm working on a dataset of banking transactions and would like to find recurrent transactions. I've been mapping transactions per merchant in timeseries, and tried to use acf from statsmodels.tsa....
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MAPE over 100% after normalization of dataset

I try to forecast power demand for next 24 hours. Years 2017 and 2018 are my training set, 2019 is test set. I use multistep vanilla LSTM . In first step I used original data with any preparation and ...
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Predicting future of power consumption in repeating manufacturing process

I have this situation. We are tracking the power consumption of an industrial machine and by looking at the power consumption (in watt) we're trying to predict whenever something will break resulting ...
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time series forecasting of time to leave for multiple customers using one model

I am a beginner in the domain of forecasting and I was wondering if such a problem could be solved with time series analysis : given customer historical data of taxi pickups,along with the weather ...
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Multiple time series sequence prediction for multiple multivariate time series

My question is somehow similar to this question, but not satisfied with the answer. I have 100 samples, each sample consists of ...
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1answer
14 views

Using a past time series to predict how a present time series will pan out?

Let's say that I have past data indicating how some time series panned out. Now I also have the beginnings of a new time series that I expect to pan out in a similar trend to the old one. What are ...
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GAN training the average of my train data

I have been training a GAN with 1D convolutional layers on sinus functions. However if I start varying my sinus (random amplitude for example), the model generates only the average of the random range....
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Trouble understanding how I could use multivariate time series to predict when an error will occur?

First off, I have very limited knowledge statistics-wise and am more of a coder. I was thrown into a large scale project and could use some guidance. I have a large multivariate time series dataset ...
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I want to know which machine learning algorithms can be used for trajectory classifications?

I am working on project for clustering of air objects based on their trajectories. Like I want to train a model on dataset of different flying object's trajectories so later I can predict what type of ...
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How to train LSTM for multiple time series with multiple variable and diferent size of time series?

I have a dataset of aircraft messages wich have an column that identify each aircraft example: idaircraft=1 , timestamp=340503404, altitude=xxxxxx,longitude = xxxxx, latitude = xxxxx, Touchdown = ...
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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 ...
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1answer
23 views

Python: forecast unevenly spaced time-series?

My data has timestamps corresponding to the failure occurrences of a specific component in machinery. The timestamps are not uniformly distributed. My question is: 1) what methods can I use to (almost)...
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time series forecasting with breakup of among variables

i am building a time series model where i want to predict number of defects and then breakup of those defects in some categories. example:(2019 data) ...
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open source time series sales data for forecasting

I'm looking for open source time series sales data (past 2-3 yrs or more) that contains at least the following variables. ...
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25 views

Dealing with low variation data

So my current project involves using a neural network to try and predict the probability of a player getting a kill in a first-person shooter. I've recorded a number of features that should be ...
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Handling missing timestamps in LSTM model

I have 10 minute SCADA data of wind turbines and many timestamps are missing in between because of regular shutdown and weather conditions. My objective is to predict gearbox failure. How can I handle ...
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How does inverse of logistic function produces “linear relationship”, (so we can use least-squares)

I am reading about time-series analysis in "A First Course on Time Series Analysis". The book reviews the logistic function ($f_{log}(t)$). Part 1.6 (PDF page 16; book page 8; screenshot below) ...
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Deriving features from Fault Occurances

I want to build a time-series out of a series of occurrences. My input is actually a DateTime value corresponding to failures in a specific component. ...
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lags and rolling window in Azure AutoML Time series forecasting

I'm following this tutorial to try Machine Learning AutoML Forecasting. In the several parameters we can submit to the AutoML experiment, we have these ones: target_logs; target_rolling_window_size;...
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How to predict rates in specific time period in time series data using python?

I Have time series data from 2005 to 2019, belongs to gaming website which has many users from around the globe logging into it, given that the same login_location entry occur many times in the same ...
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Changing time period between data points based on how far into the future has to be predicted

I'm trying to predict future values using time series data. If I want to predict further into the future, say a couple days ahead, should I use a certain period of time between data points? Is it ...
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35 views

RUL prediction without failures in historical data

I have faced in the past some problems of predictive maintenance where I had historical sensor data with failures. With this kind of dataset, you can calculate the RUL (Remaining Useful Life) and ...
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Data Conversion to Time Series in R

I am having Sales data of 2018 and 19. I need to convert to time series. The data is not having daily sales View(df) Sales Date 75606 11/01/18 95620 16/01/18 55666 ...
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Trouble with plotting time series data

I am doing something wrong with X in these time series plots, but what? This code using plotly yields the following ridiculous time series line plot: ...
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Detecting off state in the magnitude of accelerometer data?

I have a univariate time series signal. It's the magnitude of an accelerometer attached to an engine. I need to create an algorithm to detect off state, please see the black lines in the image below....
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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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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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time series prediction using arima and non linear trend and too much residuals

I am working on forecasting an financial index, i tried decomposing the time series using : ...
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9 views

LSTM's with variable size input features + how to do embeddings with (x,y) coordinate systems

Background : So I have a dataset of x,y positions of dancers dancing("doin' their thang!!"). Some sequences of the dance are with 8, some with 5,4,8, upto 16. So, I am trying to do something like ...
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Dynamic time warp z score normalisation not working

I have data that looks like this A bit of background, these are soil moisture graphs of different depths. I wish to investigate how long it takes for water to drip down from one depth to another, ...
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23 views

Forget Gate in Long Short-Term Memory

What value does actually LSTM forget in a training phase? for example, I do have a surface temperature data for 10 years. Then I made them as a training data for building my neural network using lSTM ...

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