Questions tagged [time-series]

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

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times series prediction with several regressors( using R)

Absolute beginner here. I'm trying to use a neural network to predict price of a product that's being shipped while using temperature, deaths during a pandemic, rain volume, and a column of 0 and 1's (...
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195 views

TensorFlow Time Series Tutorial Enhancement Gone Wrong

I’ve been following this time series tutorial for Tensorflow… https://www.tensorflow.org/tutorials/structured_data/time_series And it was going well and seemed to work ok. I substituted with my own ...
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Loss drops to NaN after a short time for a time series classification

here is my model code for a binary classification of a time series: ...
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13 views

Should I use an LSTM model when the outcome is a different variable from the training data?

I am trying to model a health outcome as a function of climate variables. I have many observations of health outcomes at different times and locations (but NOT a sequence at one location). For each ...
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determining size of batch, time of sending and memory in to send from scala to ML section

I have a time series (sampling time: 66.66 micro second, number of samples/sampling time=151), I would like to determine some anomalies in them, the inputs are made by scala customer. would like to ...
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51 views

What is the right way to make prediction with time series forecasting models?

I'm into ML and data science for a while but now I started exploring time-series forecasting, and I have (lets say) a simple question: What are the features/inputs for the time-series forecasting ...
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492 views

k-Nearest Neighbours with time series data - how to obtain whole-time-period estimators

I have a large dataset for the activities performed by multiple staff in a factory over a long period of time - 01/01/2017 - present. The activities performed by the different staff are recorded at ...
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1answer
54 views

What kind of algorithm should I use to build ML model that can predict just next reoccurence of an event in the future (at irregular time interval)?

I'm quite new to machine learning and statistics. I've a dataset from some ecommerce sale's history. It's almost 2k instances, and features include personId (string), productCategory (string/...
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Prediction method when the time series is not sequential?

I have multivariate time series data consisting of monthly sales of contraceptives at various delivery sites in a certain country, between January 2016 and June 2019. The data looks as follows: The ...
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1answer
393 views

ML methods for prediction, using categorical variables and time

Most of the time series analysis tutorials/textbooks I found time series data, usually deal with continuous numerical variables. I am currently trying to solve a problem that deals with multivariate ...
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Selecting the best model parameters from grid search SARIMA [Time series]

I ran a manual gridsearch of SARIMA across several parameters and now I have 7875 rows of scores (RMSE, MAE, MAPE each) from it. These were the parameters (30k+ permutations) I ran a grid search over- ...
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1answer
67 views

How to measure/rate the effect of a exogenous covariate in a ARIMAX Model?

I have an ARIMA model, I'm trying to figure out how much an external variable (exogenous covariate) could improve the forecast, so I need to "synthesize" a rate that tell me the usefulness (or impact) ...
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Sensorfusion: Generate virtual sensor based on analysis of sensorsdata

I have a steam engine which is equipped with the following sensors: temperature sensor in the boiler room temperature sensor in the heating room pressure sensor in the boiler room rotations-per-...
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How can I use a prediction model (e.g., ARMA model or LSTM) for multi-variate data?

I have had a dataset below: sensor1 sensor2 sensor3 ... 2021-01-01 1.32 2.2 1.0 2021-01-02 4.3 2.0 0.8 ... ... I know ...
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Recurrent models for asynchronous / mixed frequency time series

What are some of the RNN/LSTM models for handling mixed frequency/asynchronous time series data, such as macroeconomics, financial, precipitation, etc.? So far I have found phased lstm from a similar ...
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LSTM decoder with 2d's input

I am developing a CNN-LSTM autoencoder in pytorch to predict time sequences. The CNN input is a RGB image: RGB image => tensor[Batch size= 4, channel = 3,width= 256, height=256] and the output is ...
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1answer
63 views

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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1answer
118 views

Building Timeseries models for stock trading having multiple stocks

I have gone through some of the tutorials on the timeseries and all of them have taken one stock for the timeseries and tried to forecast it. My dataset contains many stocks for the time period(each ...
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1answer
236 views

Voting classifier using grid search for Time Series

I have three models: Arima Auto ARIMA Double Exponential Smoothing I would like to apply an ensemble method - a voting method and allow the classifier to learn weights for these three models. I ...
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39 views

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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Prediction Intervals on (Multi-Step) Judgement Forecasts

Are there any R packages available or general methodologies for calculating prediction intervals on judgment-based forecasts? I've looked at Hyndman's text and the R forecast package - which will ...
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5answers
185 views

What is the best way to train a model?

I am trying to train my model for sports predictions. The data frame is as a below given example: ...
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2k views

Forecasting Multiple (few hundreds) uni-variate time series with inflated zeros

Hello Practitioners, Being a newbie seeking help to gain experience in Data Science. Lets take a scenario where a big company wants to forecast its sales (a specific product) across different stores ...
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1answer
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Monthly trend with fb prophet-Interpreting the graph

I have monthly data with month/year in one column and price on another. I would like to get a yearly trend with fb prophet library in python (how to use monthly data with the library is explained at ...
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1answer
239 views

Stationary time series for clustering algorithms

I have a set of time series data that I would like to feed into a clustering algorithm (like k-means, using dynamic time warping as the distance function). After standardizing the data with mean 0 and ...
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1answer
61 views

Is it a good practice to evaluate model performance by comparing the metrics of rescaled (inverse transformed) predictions and true target values?

I am now working with a Linear Regression for a time-series regression problem (I am sorry that I cannot say too much about the problem and feature vector due to NDA). I scaled both the input values ...
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How to improve geom_line plot in R: linetype and x-axis detail to show each year

Context: I'm attemping to plot time series data in R with geom_line using the package ggplot using the code below. See png file for outcome: ...
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1answer
56 views

time series prediction using arima and non linear trend and too much residuals

I am working on forecasting a financial index, i tried decomposing the time series using : ...
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1answer
818 views

Train LSTM model with multiple time series

I am predicting energy usage for a bedroom within a school residential building with date, temperature, and humidity as input features, using 7 time-steps and ...
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16 views

LSTM Timeseries Forecast with long-term, variable forecast horizon

In my graduation project, I use sensors to collect power usage data for home appliances with 5 minutes intervals, I want to create an ML model that takes in a variable number of values (len(dataset)) ...
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55 views

Var_Imp Algorithms in Pred/Class Problems: Can I use it in TS Problems?

OBJECTIVE OF THIS POST: Solve a query about the possibility of use prediction/classification variable importance tools in a time series type dataframe. Collect the largest number of variable ...
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150 views

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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1answer
162 views

Predicting churn - deal with missing dates in time series and improve modelling result

This is the follow up question for General approach on time series for customer retention/churn in retail. I have a time series of data in the following form: ...
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28 views

Preprocess multi-sample time series data: encode each sample separately or in aggregate?

Let's say I have 3 dense sequences of uniform length. Should I fit a scaler on them separately or together? ...
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11 views

identifying time series with threshold breach potential

(moved from stackoverflow.com) Hi all, I'm trying to solve a following problem. I have a set of various devices feeding their readings into a system where they are stored as time series: timestamp, ...
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10 views

Forecast methodology for geographic variables that are somewhat related

I'm creating time series forecasts for different geographies and wanted an expert opinion on how I can take into account geographic relationship to improve my model. Is there an algorithm that's ...
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2answers
68 views

How to find vertical clusters in 1-D data

I have residuals of a multivariate time series data obtained from sensors on a server.spikes in the plots of residuals indicate abnormal server state. I want to cluster the data into vertical clusters ...
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1answer
637 views

Timeseries VAR vs VARMA model: issue in time to fit model

I want to use VARMA model on a data of about 80000 samples with 10 features. I tried using VARMA model from statsmodels with p=50 and q=10 but it is taking too much time to build the model. I tested ...
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Data-driven industrial kiln drying of wood [closed]

As part of my project, I was commissioned to develop an algorithm capable of optimizing the operation of a wood dryer based on input data (including temperature, relative humidity, wood moisture ...
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13 views

Multi-step time series prediction using multivariate input to get multivariate output

I am experienced in ML for tabular data but new to time series, so I am hoping to frame my question properly. I have this data series in this format: t a b c d e f 0 1 2 3 4 5 6 The columns ...
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Conditional variational autoencoder: Feeding labeled MNIST to encoder with Keras

I am looking for a code implementation of a CVAE using MNIST in Keras. I found this Youtube video: https://youtu.be/8wrLjnQ7EWQ that does VAE, but I am not sure how do I convert this and make encoder ...
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13 views

Can we add positional encoding to time series input for time series prediction?

I want to use classical machine learning models such XGBoost for my time series prediction. Since the input data for XGBoost/sklearn based models is 2d i.e. (n_samples, n_features), I want to encode ...
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1answer
26 views

Selecting only a certain number of top features using tsfresh

How can I select the top n features of time series dataset using tsfresh? Can I decide the number of top features I would like to extract?
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57 views

1D CNN time series classiifcation : ValueError: Shapes (10, 10, 8) and (10, 8) are incompatible

I'm working on a time series classification using ASHRAE RP-1043 chiller dataset which has 65 columns and more than 3000 rows for each chiller fault and normal condition. And I have used 1D CNN and ...
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2answers
598 views

Is it meaningful to use word2vec for non-string inputs like time series analysis?

I am working on a project that detects anomalies in a time series. I wonder if I can use word2vec for anomaly detection for non-string inputs like exchange rates?
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153 views

Clustering time series based on monotonic similarity

Context I am involved in the task of clustering 1500 time series of 500 observations into a few clusters. The time series share all the same observed properties at different spatial locations, but ...
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1answer
76 views

Time Series segmentation

I have a time series graph that is segmented into a few parts based on the maintenance day. You can think of it like vertical lines appearing out of the x axis which symbolize maintenance at the date. ...
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1answer
46 views

How to Manipulate data for multiple visits per person?

I have a query to solve. I have data regarding customers and number of visits done to them. These are in two tables. So I want to join two table and create different features so that I can find better/...
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13 views

How to use one hot encoding with time series data ( arima eg)

I have cumulative number of medical cases weekly for 60 weeks and categorical data on week wise events that occurred. I’m trying to analyse which event may increase or decrease the cumulative cases. <...
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124 views

How to deal with different length entities in a Keras DataGenerator?

I'm solivng a prediction problem where I need to predict the demand of multiple articles based on their performance during the last 7 days. To get the most out of the data I am trying to implement a ...

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