Questions tagged [time-series]

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

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Take several points for each period in a machine learning model

Problem presentation I am working on a prediction model where I must find out if a boat will go back to the same offshore workplace after spending time in a port. When a boat is in a port, it can stay ...
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Time series classification of different amount of objects / Classification based on trajectory

My question is a rather open one, I have a problem that I am looking for solutions or problems that are alike. I know what format the data (which I do not have yet) will take. My goal is to find ...
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How to use the Keras self-attention modules

Is there anyone having experiences with the keras_self_attention module? The module contains SeqSelfAttention ...
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16 views

Predict time to send mail

I have a dataset which contains data on when an email was sent and when an email was opened, so I would like to build a model to predict when an email will be opened. I will then use this information ...
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Time series imputation benchmark

In a work, I have to benchmark different algorithms to fill in missing values in time series. I insist on the fact that this is imputation and not forecasting. In my case, I have access to 15 years of ...
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Box Jenkins, ADF and KPSS tests on a time series

this is my first question I have the growth from Ecuadors GDP from 2000 to 2025 (Annually) and I have to predict Ecuadors GDP growth from 2026 to 2030 taking into account: Ecuadors GDP Growth ...
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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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Discrete Wavelet Transform Time Series

My problem is to cluster some time series together. But due to a huge length I was interested in using some methods to reduce the dimensionality. I was thinking of Discrete Wavelet Transform since the ...
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1answer
34 views

How to use GridSearch for LinearSVC / Random Forest with time series data

I have a question related on how to use the GridSearch to find the best models for my problem with time series data. Every 3 rows is 1 one row in the original dataset. To make my time series problem a ...
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Weighting training data from time-series

I want to evaluate whether or not it is feasible for me to train a machine learning model using sample data that I can acquire. I have many time-series, and I am able to establish groups of 'related' ...
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Neural network type question

This web link is to a site that talks about forecasting building electricity, like a time series regression concept. In the article they talk about the NN architecture as: the architecture of this ...
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Convert time series data to supervised learning problem

I have a similar dataset like the one below. Each row represents a person and there are 3 different variables m1,m2,m3 with 3 measurements each. I am trying to frame this time series problem as a ...
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How to apply Hypothesis test on time series?

I am working on a project that is related to Wikipedia's revision history. I have around 10 time-series and each one represents how the relative frequency of positive words changes over time. I need ...
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Anomaly detection between time series [closed]

I have a machine learning problem where I must detect anomalies in (let say) taxes declaration. So I have multiple rows for each enterprise, each row describing the tax declaration a the time (date,...
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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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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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Is it possible for a times series to not have any residual component after additive/multiplicative decomposition?

After conducting a times series additive decomposition using statmodels, the chart shows only a trend component, and there isn't any seasonal or residual component observed. I was wondering how ...
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Demand forecasting with marketing budget data

I'm trying to build a demand forecasting model to predict future daily orders of an online food takeout service (similar to UberEats or DoorDash). My first model uses a univariate approach, which is ...
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Performing anomalie detection on a battery volatge using LSTM-RNN

I am trying to detect anomalies in a battery output voltage for one month. I have the next data frame, as it is shown the data is collected each minute for each day so I have almost 1420 sample per ...
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Is there a way to duplicate time series data?

Context: I want to work with a corpus of ~800,000 scientific abstracts from 1970-2017 and predict trends in another corpus. Problem: There may be insufficient data to accurately predict trends between ...
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Forecasting new product sales - pre launch [closed]

Product X is due to launch in 6 months time and I am looking for a method to estimate what the sales of this product could likely be in 3 years time. Product X belongs to a category of products for ...
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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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Regression with LSTM network: use multiple time series as input

I've spent a few days on this and am starting to think I'm missing the obvious solution as this doesn't seem like a very uncommon problem. As an example dataset: I have 100 measurements with each a ...
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Detect time pattern in sequence of events

I have a time series with a timestamp and an associated event: Time Event 1 A 2 B 3 C T A I was wondering if there is a technique/method to figure out which events most often precede others in a ...
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Multiple Linear Regression With Data Over Multiple Dates

For a project I am working on, which uses annual financial reports data (of multiple categories) from companies which have been successful or gone bust, I previously created a (fairly well performing) ...
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Train MLP Neural Network on time series data?

Newbie question here but I was curious to ask if an MLP Neural type network can be trained on time series data? The dataset that I have is an electricity type data set from a building power meter and ...
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Train/validation/test and cross-validation on panel dataset

(Cross-posting a previous question from CrossValidated in case it is more suitable here: https://stats.stackexchange.com/questions/508958/train-validation-test-and-cross-validation-on-panel-dataset) I ...
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Sum of squares for matrix valued data over $\mathbb{R}$ and $\mathbb{C}$

Let us assume we have $k \times k$ matrix valued data and assume this is organized (possibly as time series): $$ M_1, M_2, \ldots, M_n $$ Now, assume we are interested in writing down an error ...
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Time series classification using CNN model, 1D or 2D?

I have a multivariate time series dataset that has the same length for each observation but looking at a different time frame (eg. One might be from January to May and another one might be from August ...
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Grad_CAM for time series

I am new to deep learning and trying to build a Grad-cam from time series data. Shape of my input sample is (188,1), its an ECG signal and I have a cnn-1D model for classification. Keras provides ...
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Predicting millions of sparse timeseries using them to help each other

This is a very general problem faced by different types of businesses. Predict the future behavior of customers over time. Imagine that we have 1 million customers with their own features over time, ...
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Can multiple time series take advantage of each other?

I want to forecast house prices market in multiple cities of the same country. I expect demography, interest rate and neighbors cities values to have an impact on my prediction. For every city, I have ...
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Loss Nan: How can I properly implement a LSTM Time-Series model with a lot of parameters?

The Problem: I am very new to TF and Keras. I am attempting to train a time-series LSTM. When using only a few parameters as a test, the model seems to work fine. Once I increase the parameters to the ...
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How to use features with lags of different lengths in LSTM?

I'm trying to predict a time series, let's say I have 3 features and a target variable. I used the standard approach when feature lags have the same length, for example, 10. Then the size of my batch ...
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Is Dynamic Time Warping a good loss function for a time series auto-encoder?

I've been trying to implement a multivariate time-series auto encoder. I thought DTW could be a good loss function but my implementation is still too slow. Anyone has some ideas of pros and cons of ...
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2answers
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PyTorch: Predicting future values with LSTM

I'm currently working on building an LSTM model to forecast time-series data using PyTorch. I used lag features to pass the previous n steps as inputs to train the network. I split the data into three ...
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Best Way to tackle to time series classification problem?

I have a dataset where the input is a dataset for ICU patients where each ICU stay has 40 features (20 vitals, 20 lab values) and multiple time steps (the stays' length is between 6 and 19-time steps)....
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How to implement a Multivariate multi-site application in LSTM?

I am trying to make a multivariate multi-site classification LSTM model using Keras. I have followed this tutorial from Jason Brownlee: https://machinelearningmastery.com/multivariate-time-series-...
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Does binning a time series with pd.qcut (using quantiles) create data leakage?

Let's say I want to predict whether a company will default on it's debt at some point in time (so binary classification) and one of the time series variables I'm using is the "revenue" of ...
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Time Series Modelling or Simple regression or something else

PROJECT: I am working on an e-commerce site where digital products can run out so there is need to reorder them 72h before they run out (reordering them sooner is not a problem but having notification ...
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statsmodels.tsa.holtwinters.ExponentialSmoothing: what do “additive”/“multiplicative” trend and seasonality actually mean?

There are additional concepts of additivity and multiplicativity for trend (trend{“add”, “mul”, “additive”, “multiplicative”, None}) and seasonality (...
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useful method for analyzing the real-time (online learning) time series? forecasting and decomposition?

Which method is useful for analyzing the real-time time series? even for forecasting or decomposition? I have a large dataset (sequential data) that need for predication analysis and decomposition ...
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1answer
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Overlaying a line graph and an area graph: adding recession bars to a time series

I am trying to reconstruct a time series graphs from FRED: https://fred.stlouisfed.org/series/LABSHPUSA156NRUG using Python. However, I am unable to get a clean figure where the time series is ...
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Which Model to apply - Time Series or some other model

I have two different use cases and wanted to know which kind of model be applied based on the two scenarios scenario 1 ) Sales is my target column and rest are independent attributes / Predictors. ...
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Features in classification problem

It's rather a strange question about feature engineering for classification problems (churn). I've read a lot of articles and tutorials for such problems, especially on telco domain. In my case the is ...
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23 views

plotting time series data using matplotlib python

I am trying to visualize Time series data is as follows following is my code to plot the data plt.plot(data['date'], data['c_16_avg_a']) ...
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Timeseries param tuning using XGBOOST

I am using xgboost for timeseries forecasting of a certain attribute while including seasonal features.Trained on nearly 4 years of data and tested on the last month. My rmse is as below : Hyper ...
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109 views

Time-series multi-step generalization from single step model

I have built a generic stacked lstm model of the form: ...
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Is there a way to pass new data to fitted ARIMA model to get predictions or I need to retrain it every time?

Is there a way to pass new data to fitted ARIMA model to get predictions or I need to retrain it every time? I need to put to production and see how it trades in real life.
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Forecasting using Boosting methods on Non-stationary Time Series data

Theoretical Noob question - Can we use boosting methods to effectively forecast the future after being trained on a non-stationary time series? Or do you train/fit on the residual of the training set ...

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