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: Why does this data have seasonality on periods where observed values are zero

I'm a novice to time series and am using seasonal_decompose() to split a time series into three components: trend, seasonality, and residuals as below: As shown in ...
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Class relationship based on location and time

I have a spatio-temporal dataset with a binary label. Is there any way I can find out if there is a relationship between the two class labels based on their location and time? Dataset columns - ...
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In ensembles combining models, does it make sense for a model to have negative weight?

I have 13 models ranging from simple models like Seasonal Naïve Average to complex models like Random Forests, The weights of the models is calculated based on the LPMinimize of the error during the ...
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Input Tensor Shape for CNN Binary Classification of Time Series Data

I want to predict whether a machine will fail based on the most recent set of measurements taken by on-board sensors. I have several dozen machines, each with a sensor that takes a measurement at ...
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Is it correct to generate similar rows by reducing the time-frame of an instance?

I'm participating in a People Analytics project with a small historic dataset that includes event variables. The aim is to predict employee's attrition. I have variables like area, dept., company, etc....
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Can a Binary classifier be used to understand the relationship between categorical data and multivariable time series data?

I'm looking for peoples advice and opinions on a certain analysis approach I'm thinking of doing for my experiment. Experiment Effectively I flash a light into a mouse's eyes and record the activity ...
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Do we need padding in global time series models?

In order to forecast product demand for a costumer, I need to predict time series of different lengths. I am using mostly global lightgbm models to do the job. In most use cases, it is common practice,...
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Unstable loss in binary classification for time-series data - extremely imbalanced dataset

I am working on deep learning model to detect regions of timesteps with anomalies. This model should classify each timestep as possessing the anomaly or not. My labels are something like this: ...
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CNN for time series: Input 0 of layer "conv2d_5" is incompatible with the layer: expected min_ndim=4, found ndim=2. Full shape received: (None, 2)

I am trying to use CNN on multivariate time series instead the most common usage on images. The number of features are between 90 and 120, depending on which I need to consider and experiment. This is ...
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Summerize features for time-series predicted result

Background Our project is to classify if the given meter is EV or not, we got time-series data for each meter, got columns like time(30min intervals), and consumption in power. We also have some users ...
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Number of CNN's for processing timeseries using 2D CNN

I am processing time-series for classification using 2D CNN model (single channel) where I have converted stationary time series data into 2D image using an "imaging algorithm" known as ...
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which code is right to do the times series analysis in this data set?

The dataset has two variables that are date (Jan 2018 to Sep 2019) and the value ($million). It concerns the value amount of air import and I would like to do time series analysis. I am a university ...
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LSTM with multiple entries per month

I'm trying to solve a problem that has this structure: Date ID feature1 feature2 Y Month1 1 1 2 1 Month2 1 3 4 0 Month3 1 5 6 1 Month1 2 7 8 1 Month2 2 9 10 0 Month3 2 11 12 1 I need to ...
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How to test and discover dependency between time series?

$x_1(t), x_2(t), x_3(t), ...$ are independent variables, and $y(t)$ is another variable I suspect to be dependent. I have dataset of the evolution of $x_1(t), x_2(t), x_3(t), ..., y(t)$ over time in ...
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Feature engineering time series data to capture a specific pattern or pay more weight to that pattern

We want to know how to apply Feature engineering(or any other ways) to time series data to capture a specific pattern like the blue line shows, the raw data is: ...
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TimeSeries forecasting with Catboost

After extensive research on both the documentation and internet itself, I found many articles showing how to fit() and predict() a CatboostRegressor, but all of them use split data for train/test (...
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Batch normalization for time series data

Could you please explain if and how to apply batch normalization on time series data? Does it differ (if so, how) from the batch normalization in images, for example. While it is not formally correct ...
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Forecasting multiple univariate time series using lagged values as predictors

I'm trying to forecast 20 univariate time series using an ensemble of XGBoost, Prophet, Prophet Boost and Random Forests. As you can imagine, each individual time series have statistically significant ...
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What is and why use blocked cross-validation?

I was reading about cross validation equivalents for time series data and found a variation called blocked cross validation. On the page I was reading it says the following: "However, this may ...
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Time-series classification in a production environment - Doubts

I created an ML model to classify five IoT signals (say A, B, C, D, and E) I get in CSV files monthly. Each signal has a value in the sampled timestamps. My questions (doubts) are: Do I have to ...
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a model to predict a day from other days?

I have the day of the month of 100 purchases made by a customer. Is it reasonable to use linear regression to predict the day of the month of purchase 101? Or what kind of algorithm should I use? How ...
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Why are LSTMs not very good in extrapolating time-series?

I was trying to train an LSTM based recurrent-neural-network to extrapolate a simple time-series. The time-series I am using a simple superposition of sinusoidal series of different frequencies. ...
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Time series forecasting with incomplete future data

I have historical daily time series bookings data. I am using the Prophet model to predict daily bookings for the next 7 days. However, I also have incomplete booking information for future dates. ...
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Correlation analysis of time-series dataset

Clarifications required to proceed after understanding the correlation analysis for processing the time-series dataset: Does the correlation analysis for time-series data need to be different than it ...
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What model should I use to predict monthly sales by products?

I am trying to predict monthly sales by product based on a plethora of variables. There are 4 predictors. One is categorical (month) and the other three are numerical. One of the variables is just ...
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What is the fastest way to detect periodicity in a binary time series?

Example, T = array([0,1,1,1,0,0,1,0,1,1,1,0,0,1,1,1,1,1,0,0,1,0,1,1,1,0,0,1,0,1,1,0,0,0,1,0,1,1,1,0,0,1]) ( T is almost a repeat of the array([0,1,1,1,0,0,1]) six ...
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How to aggregate effects of time series, VAR and linear regression on the same dataset?

I have the Walmart store data from here https://www.kaggle.com/competitions/walmart-recruiting-store-sales-forecasting/data Say I aggregated the data at date level and now want to predict sales. There ...
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How to properly use regression / tree based models for time-series data

Regression/tree-based models appear to treat each prediction as a memoryless process, namely given a feature vector $\hat{x}_i$, predict $y_{i+1}$, but previous states $\hat{x}_{i-1}$, $\hat{x}_{i-2}, ...
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Would Stacking improve accruacy if base model accruacy is not good?

Problem: I would like to improve accuracy of stock price prediction image classification model using candlestick charts. Base model: VGG16 and EfficientNet. Base model input: Two models independently ...
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how to classify sequence of ball coordinate x,y with LSTM

I am working on a problem where I need to classify the sequence of ball coordinates X and Y with LSTM or another model. I have two classes rally(1) and not rally(0). I want to get as an input model a ...
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linear regression - at future time points

I have a dataset of customer transactions containing revenue, customer id, region, product category, product id, support team, date of transaction etc. The data ranges from Jan 2017 to Nov 2nd 2022. ...
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What is the fastest way to detect lag and calculate cross correlation of two binary time series?

Example, arr1 = array([0,0,0,1,1,1,1,0,0,0,0,0,1,1,0,0,0,0,0,0,1,0,1,1,1,1,1,0,0]) arr2 = array([1,1,1,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,1,1,1,1,1,0,0,0,0,0]) arr2 is almost perfectly correlated with ...
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Data stock pricing Python, unsuported string error

I have the data of stock prices and I reduced it to a smaller data set. ...
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How to find top k most co-related time series among a large set of time-series

Background: I am doing a project on epidemiological time series fore-castings, in which I am required to make predictions of a disease based on several symptoms. About the dataset: The dataset of ...
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Difference between batch_size in TimeSeriesGenerator and model.fit batch_size

im wondering if there is a difference between the batch_size set in the TimeSeriesGenerator and the batch_size in the model_fit. I create some RNN Forecasts for timeseries. ...
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What type of machine learning Is suitable on Arduino to predict anxiety with certain parameters?

I am working on a project that involves the prediction of three different anxiety states according to the galvanic skin response, peripheral skin Temperature and Heart rate (BPM). In order to learn my ...
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Creating a dataframe using roll-forward window on multivariate time series

Based on the simplifed sample dataframe ...
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Is it possible to train a RNN using multiple time series?

I have multiple time series (about 200) of soil moisture behavior after saturation in different soil types. They are all the same length and nearly the same shape, differing only in their ultimate ...
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Using Fourier Terms and lags in Time Series Forecasting with R

I wish to use Fourier terms as predictors in my XGBoost model. My date is weekly sales from the timetk::walmart_sales_weekly dataset in R. To use Fourier terms there are two arguments required in the ...
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How to choose the threshold for recurrence plot?

Context: I'm doing the final project for my bachelors and it's about identifying apnea in eeg signals with a CNN. I'm dividing the signal in equally sized segments and then generating images for each ...
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Sparse set of risk features for churn style model

I'm looking for different approaches to formulate the following problem: Say you have a group of features on customers for your shop. These are all interaction features with your website, and some ...
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Time Series figures finder

I have been working on a continuous Time Series with the objective of creating an algorithm that finds some specific kind of figures defined by the graph of the curve. I partially solved the problem ...
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Why so discrepancy between ARIMA and LSTM in time series forecasting?

I have this time series below, that I divided into train, val and test: Basically, I trained an ARIMA and an LSTM on those data, and results are completely different, in terms of prediction: ARIMA: ...
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Work with tabular data and many time series

I'm working on a credit default prediction problem. I have bureau data that contains general information on past loans (loan amount, interest rate, etc.). And credit information for every month. How ...
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Multivariate time series prediction with categorical features

I have a time series problem where I have to predict a t+1 value having a set of features representing the status of the problem. for instance my dataset is: ...
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How to convert postgresql tabular data to a numpy array?

I'm completely new to data science and I have a problem that I need help in resolving. I have time series data (with 87 million rows currently, though that will grow) with x, y coordinates, a ...
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How to scale a subset of data with respect to the entire dataset

I am developing a financial time-series prediction model using sklearn using StandardScaler for scaling purposes. I train a model, and then use the model regularly ...
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Need to predict account activity based on history

I have three datasets, one containing app status information, the second containing account info, and the third being activity logs for the past couple years or so. The activity logs contain records ...
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How to interpret training and validation loss of DeepAR?

Please bear with me. Its a long but complete post. My questions are: Why does the training loss start to osccilate wildly after some epochs? It is because it has jumped out of a local minima? I tried ...
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How to make ARIMA model out of sample forecasts with exog Fourier terms using weekly data? (Python)

I'm a bit confused on how to make out-of-sample predictions if I have Fourier terms included in my ARIMA model. I am using Fourier terms to model annual seasonality as per the advice given in ...
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