Questions tagged [time-series]

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

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Time-Series Similarity & Clustering

I need to investigate Python-based tools for time series clustering and / or similarity matching on specific dataset and evaluate different approaches. Could you please suggest approaches that could ...
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Training XGBoost on time series features of varying sample length

I have some time series data that contain features that that go back anywhere from 5 to 50 years. I've considered imputation (e.g. taking the mean), but I'm not sure it's feasible to impute such large ...
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How to calculate total observation time per focal individual across entire observation period?

I have a large amount of data for a set focal individuals that were under observation on a daily basis over three years. I am trying to calculate total times visible doing an activity and not oos (out ...
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Border values at which timeseries decrease and increase

I have a timeseries data of signals in stock market ([-1, 1]) and I want to find mean values at which I have down trend and upwards trend. I already used Moving ...
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Testing the impact of events on time series

Context I am working with product data for a retail company. I have the daily impressions (number of times it was viewed online) for all products over a 30 day period (can get more data). Here is the ...
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LSTM Forecast timeseries with Hyperparameter Tuner (Random Search) from Keras

I want to predict a timeseries with a LSTM Model. I try to use the Tuner from Keras to find the best hyperparameters. data_example: date value 2022-01-02 600 2022-01-03 640 2022-01-04 605 ... ... ...
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What this kind of timeline chart is called?

I created this timeline chart myself because I couldn't find a library that implemented a similar kind of chart (I checked Plotly and Seaborn). I'm interested in finding out what other people call it. ...
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Python (S)ARIMA models completely wrong

I have some time series, like this one: I want to predict future values, so I splitted in train/test (70/30) and I created several ARIMA models, however they are all completely wrong (or maybe I am ...
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Extract feature from time series dataset used for classification(not group them)

I have time series data and would like to try to capture all of the features for them (for example, something this time related to other times or something else), but the current timeseries feature ...
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why seasonality period is same for additive and multiplicative models for seasonal_decompose in stats models?

seasonal component with 'add' model seasonal component with 'mul' model Is this behaviour accidental with this dataset or there is some reason which can be mathematically proved?
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clustering time series with different sized time series

I have read this article on towardsdatascience and they teach how to cluster time series using the DTW distance and the TimeSeriesKMeans from the tslearn.clustering library. I also read the official ...
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Visualize time series data

I have a time series data set with 3 parameters and 5 dates per time point which I would like to visualize. The problem is that the date time points (year) are not equal over the parameters: ...
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Movement Analysis (Unsupervised Learning)

I have some data on the movement of a drone, D piloted by multiple people, P. Multiple metrics on the its motion is recorded, example speed, acceleration, elevation, angles which will be denoted by X1,...
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Is it possible to "link-couple-connect" certain inputs with outputs in a MIMO seq2seq LSTM model?

I have a seq2seq model with encoder and decoder as LSTMs which takes INPUT as the past 4 days of building data (weather data, 5 zones data like occupancy, internal loads, indoor air temperatures, and ...
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How does one perform a Canova-Hansen test in Python?

I am referring to the documentation here, but it does not give many examples on how to actually perform the test. I have a pandas dataframe with two columns: Column 1 is first day of every week, ...
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Smooth Ternary (binary but 3) Time Series Data

Suppose we have a problem where an autopilot algorithm looks at a dash cam video with a pedestrian and outputs 1 of 3 predicted pedestrian intentions: [-1, 0, 1]. <...
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Explanation for a parameter in ADTK package

I have a doubt with LevelShiftAd in ADTK package. Can someone explain the parameter c (Factor used to determine the bound of normal range based on historical interquartile range) please?
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Creating a prediction matrix for kernel regression in numpy

I need to create a rectangular matrix from timeseries data which is T time bins by L, the sum of the length of the set of events of interest occurring in the timeseries within certain times referred ...
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predictions based on irregular repeated measures?

I need to make a model that predicts certain medical outcomes based on the answer to health-related questionnaires. Providers have patients fill out these questionnaires more than once, at irregular ...
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What do results like these imply in a LSTM classification problem?

I am training a LSTM network to learn from multiple time series, and the output from the network should be binary (or equivalently a probability score between [0, 1]...
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Why is Orange (CSV import or Line Series) doing some weird rounding on my data?

The data I have is in tab-separated format (exported from MetaTrader5): ...
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Create my own timeline by collating data from literature

I'm researching various paleo-events (climate, geomorphology, etc) from different articles. I would in turn like to collate the findings into one comprehensive timeline. Example: I have articles ...
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Is it possible to get by month simulations in orbit?

I am looking to use Uber's Orbit (https://uber.github.io/orbit/) to get time series predictions. By default, orbit will output point estimates with prediction intervals defined by the user. What i ...
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Detrend a time series

I am fairly new to forecasting and I am trying to create a demand forecast for my organization; I am following the methodology outlined here. In step 12 of the process, the author subtracts the trend ...
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XGBoost training on sample of time series data

I am new to XGBoost and would like to use it on a time series dataset. Here is the scenario I'm faced with: The data set contains N samples of length T, with N>>T. I'd like to train an XGBoost ...
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Predicting time series

I have a very large dataset (about a year of driving) which contains the following features: datetime with 1 second resolution - speed of car - GPS coordinates of the car in each time-step - average ...
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Time series prediction with feedback

I have a multi-dimensional data, but it misses target variable. I want a model to predict a value for this data that could be passed to my loss function which can be then used as a feedback for ...
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How to prepare data for multivariate prediction with irregular window size for prediction?

I have a dataset of different products and their possible configurations. I want to build a model which can predict the next part for the product given the previous part/parts. This model will be used ...
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How is the encoder state passed to the decoder (LSTM, Keras)

My understanding is that in the Encoder Decoder LSTM, the decoder first state is same as the encoder final state (both hidden and cell states) . But I don't see that written explicitly in the code ...
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Is removing huge data for time series model right?

I have time-series data that contains nearly 90% zero or null values. Is removing these values correct? After removing them, the dataset will not have a constant time difference between samples.
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Predict time series classes 15 min before

I have multivariate time-series as input and I need to predict an event ("active" or "inactive"). The occurrence of class needs to be predicted (at least) 15 minutes before it ...
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Detect data (web textual content) age

This is a broad question and maybe does not have an answer but I will try. I have been thinking of some techniques to detect the date of publication of public data in the wild of the internet. Without ...
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Taxonomy of train-test split approaches

I am looking for as close as possible for a exhaustive taxonomy of each train-test split approach. For example, the 3 main splits that come to mind are: A non-time based problem - would lead you to a ...
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Use two different but correlated time-series signals as two different samples to train a model

I want to train a forecasting model for signal A initially. In the future, forecasts B and C may be required. These are all financial time-series signals with the same resolution. The issue I am ...
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Sorting column in dataframe in each group in R

I have a timeseries dataset with price variables and want to sort them. The time ist structured into quarter hours. So each group is marked by 4 rows. I want to sort the first two columns within each ...
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Forecasting physical performance of young football players

I have a dataset containing physical testing data from a football academy, including sprint tests, COD test, endurance tests, strengths tests, and anthropometric features. The dataset contains all ...
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Why is my LSTM prediction is saturated and have bad prediction?

I am new to deep learning. Currently, I am trying to predict torque based on its past values using an LSTM model. There are two datasets (generated from a scaled test), one with wear and the second ...
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Recent research on solving "inverse" ODE problems with neural networks?

I come from a physics background, and I am not familiar with the state-of-the-art research in solving ODE optimization problems with NNs. Let me briefly introduce this so-called "inverse" ...
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SOS / EOS tokens to encode fixed-size 1D real and complex-valued short signals with seq2seq

Are there any standard or recommended SOS (start of sequence) and EOS (end of sequence) tokens for seq2seq encoding using RNN/LSTM/Transformers applied to real-valued and/or complex-valued 1D signals (...
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How to use multiple parallel inputs for time series forecasting -- Pytorch

I'm currently working with the ECG recordings of several patients. I have the amplitude of the ECG for around 48 patients over the span of one hour, and I want to be able to forecast their future ECG ...
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Surrogate model for [parameter vector] to [time series]

Say I have a model $M$ that takes in a parameter vector $\beta$, and produces a (numerical) time series. This could be a complicated model (e.g. a bespoke enzyme reaction model), or something simple ...
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is there a standard metric to check volatility between every week forecasting results?

I have fitted arima, tbats model on my dataset and forecasted results for 12weeks. my forecasting results from 1st week to 2nd week differs a lot. I checked how much is the variance(volatility) ...
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Need help diagnosing a training curve for LSTM-network

I am doing time series prediction using and LSTM-network. The dataset is divided into a training, test and validation set. The LSTM-model structure (number of neurons and layers), learning rate, batch ...
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Neural network training for multi time scale (fast and slow) data

I have a background in dynamical system control and I am new to machine learning field. In control, we sometimes have a system that has multi time scale dynamics, e.g., some states evolve much faster ...
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Time Series Algorithm Question With "As of" Data

I have data in the following format: As Of Date Calendar Day Demand 7/18 8/1 41 7/19 8/1 49 7/20 8/1 52 7/21 8/1 55 7/22 8/1 62 7/23 8/1 66 7/24 8/1 61 7/25 8/1 79 7/26 8/1 87 7/27 8/1 95 ...
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Data augmentation for region based time-series binary classification with contained feature values

I am working on time-series problem where I have feature values for various timesteps that are fed to a LSTM deep learning model. My features are all values within the range of [0, 1]. It is a binary ...
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Classification, timeseries with different lengths and more than 1 feature (more than 1 time series per individual)

What are the primary options regarding classification problems with time series with more than one feature and different lengths? So far I've read of k-means with dtw, but haven't seen it applied to ...
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How to guarantee Catboost output being integers?

I am trying to use Catboost regressor to predict next integer of a sequence of integers. I am using 4 numbers as input to predict the fifth. My problem is, instead of integers, the model predicts ...
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Can I get good prediction results without shuffling my training data?

I am currently trying to estimate joint torque from 8 muscle activation values as input in an LSTM model and I'm treating my data as time series. Currently my model performs pretty well when shuffle ...
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Predicting stock price using stock news sentiment analysis

I was trying to understand how can we use stock news sentiments to predict stock price. I was going through the video claiming to utilize stock sentiment analysis for stock price prediction. The video ...
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