Questions tagged [time-series]

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

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24 views

Neural net performance using rmse

I am trying to build a NN which can predict exchange values. I am quite new to R and NN and I don't quite understand how I could improve the performance metrics of the neural network. I have tried ...
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1answer
58 views

I'm trying to do a time series model without a datetime field in python. Is this possible?

I have a dataset with data like this: Day Revenue 1 1.2 2 1.5 3 1.1 4 1.34 I want to do a time series model on it, but am ...
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80 views

How can I plot a line for time series data with categorical intervals in R

I am working with single time-series measurements that I want to plot for the time window of about 1 week. This is the data I am working with. This is my R script: ...
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1answer
25 views

How to get periodicity from timeseries data?

I would like to create a recommendation system for a smart home application. I gather the data in a time-series database. The app monitors the on/off state of a smart lamp and can create daily ...
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51 views

LSTM with multiple entries for the same timestamp

I have a dataset where I have multiple entries for the same timestamp and I want to use LSTM to forecast the next timestamp given the previous 5 timesteps. From https://machinelearningmastery.com/...
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1answer
40 views

Predicting sparse time series data

I have a dataset of a couple of EV charging stations (10 min frequency) over 1 year. This data consists of lots of 0s, since there is no continuous flow of cars coming to charge, but rather ...
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Modeling Scaled Residuals

I found a good model for a time series forecasting problem I have but it doesn't allow for covariates, which I need to include a few of (date-based events). The approach I came up with to deal with ...
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10 views

Do Any Frameworks Provide Better Support for End-To-End Integer-Based Feature Engineering, Modeling, and Inference?

A retail enterprise I work with with wants to switch from its home-grown time series data analysis and prediction system to something more established and with community support. One unique feature ...
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1answer
47 views

Logistics Demand Forecasting with 20k Different Time Series

I'm trying to tackle a very challenging problem and I would appreciate your help. My organization has a lot of different items which can be demanded by our clients. Those items can also be returned ...
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21 views

How to put marker on time series training set

My input is this picture And I would like to put markers on it and use time series with markers as a label Two picture are not scaled. Main point is I would like to train RNN classifier and let it be ...
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35 views

Embedding a categorical variable and concatenating with a numerical variable, in a many-to-one sequence problem with multiple features

I have a small data set where I track 4 variables across 4 time periods, 1 categorical and 1 numerical variable. Below is picture of data set that I am using: cat1 - Categorical variable encoded num1 ...
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11 views

Time series packing algorithm for load balancing/smoothing

I'm looking for pointers to implement an algorithm which takes many data series (load profiles) and packs them into a fixed number of containers in order to minimise peaks and achieve the most even ...
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11 views

How can I intuitively calculate the accuracy of my financial prediction model?

I've built a SARIMAX model based on my personal spendings record as a college thesis and have reached a point where I'm pretty content with how it turned out and am getting ready to start writing the ...
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34 views

How to calculate MAE and threshold in a multivariate time series

I'm trying to understand how to calculate the MAE in my time series and then the thresholds to understand which of my data in the test set are anomalies. I'm following this tutorial, which is based on ...
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1answer
40 views

Problems with Concatenating Embedded Categorical and Numerical variables for LSTM use

I am new to here and new to Deep Learning too, so apologies in advance for any ill formatted code or wordings. I have a data set where I track 4 variables with 2 categorical and 3 numerical fields, ...
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11 views

How to decide the right granularity in time series?

I am wondering how to decide right granularity I should use for time series if I have data for hourly as well as daily and given that I don't have business constraint which asks me to do forecasting ...
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17 views

Bidirectional LSTM usage with sensor data

I am applying a deep LSTM network in order to classify time-series data from different sensors. In the field (energy) I often see the research using bidirectional LSTMs for forecasting. I don't get it ...
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13 views

How to train a deep neural network with time-series images and unbalanced dataset?

I have images that represent a fixed-length time-window of different serials. Serials have time-series of different size, so e.g. serial1 has length 30, serial2 length 110 and so on. I have multiple ...
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2answers
114 views

Autenocoder and anomaly detection task

I'm trying to create an autoencoder for the anomaly detection task, but I'm noticing that even if it performs very well on the training set, it starts to stop recreating half of the test set. I tried ...
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22 views

Is it mandatory to have a DateTime index for a pandas dataframe to run ARIMA models on it?

I'm trying to learn about Arima models. In every tutorial, the Dataframe considered has a DateTime index. When I looked upon the mathematical formula for Arima model, it only depends on the past ...
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1answer
41 views

Why is shuffling timeseries a bad thing?

I'm trying to understand precisely why it is a bad idea to shuffle time-series when splitting train and test data. Like, what is false about shuffling time-series? How does it tamper with the model?
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1answer
233 views

Problems to understand how to create the input data for time series forecasting with a recurrent neural network in Keras

I just started to use recurrent neural networks (RNN) with Keras for time-series forecasting and I found this tutorial Forecasting with RNN. I have difficulties understanding how to build the training ...
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55 views

YOLO for timeseries, Error when training

I'm currently trying to implement the YOLO object detection algorithm for the localization and classification of events in time series ('signals'). To do this, I have defined a custom loss function, a ...
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How to modify a Convolutional Neural Network architecture built for a univariate time series to multivariate time series?

I have built a CNN (in combination with a LSTM cell) that takes 1D time series-like data as an input and performs classification. I am obtaining a good performance, but the complete data has actually ...
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21 views

Does a 3-D input shape (time series with features) to an LSTM Model Evaluate the Label at each Time Step?

I have a problem similar to the one posed in this video: https://www.youtube.com/watch?v=flMCYqIn3eg In that video, she had a set of data on a number of individual debtors and needed to find out if ...
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Using forecasting values of wind speed at different hours in the future to predict power output at different hours in the future for a wind turbine

I need to design a neural network model for a wind turbine which takes as input the forcasting values of wind speed as follows : windspeedPlus001hr , windspeedPlus002hr , windspeedPlus003hr , ...
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15 views

Can LSTM be used to predict value as regression problem?

I have time-series data as shown below. Which model is generally preferred if grig_id is needed to be predicted? Is it possible to use LSTM with a sigmoid ...
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26 views

Time-Series analysis with small data set, but long sequences

I'm working on a time-series classification problem. There are 3 classes. Dataset consists of 6 sequences from each class (total 18 sequences). Each sequence is 19,000 in length. What are some time-...
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1answer
68 views

Understanding time series anomaly detection using Autoencoder

I'm studying how to detect anomalies in the time series using an Autoeconder. In particular, I'm following the guide posted in the Keras website, but I don't understand why they are creating and how ...
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1answer
24 views

How to compute time-lagged correlation between two variables with many examples at each time t?

I have a dictionary of following form: datetimes = {year : {name : (score1, score2)}} #there are 50+ names/year So, essentially, I'm trying to get an aggregate ...
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13 views

How meaningful are the results when you difference the time series dataset before clustering?

On a certain task where I need to perform K-Means Time Series Clustering with DTW algorithm, I would like to know how credible the results are when performing clustering on the original vs a dataset ...
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1answer
23 views

what indicators can be used to classify a group of stocks?

I am working with a dataset contains the daily return time series of 50 different stocks, I want to divide these stocks into several different groups. My idea was to make a new dataset contains some ...
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What models should I try with a time series database? [closed]

I've acquired and cleaned a dataset that shows statistics from every county in New York State during 2010-2019 focusing on the NYS School Aid correlating it to other growth and criminal statistics. ...
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1answer
42 views

Create a single time series plot of multiple devices [closed]

I have a dataset where there are stored the measurements of 30 devices. Each device has about 4000 values and it is structured as well: ...
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15 views

Finding optimal time series using convolution [closed]

we logged sensor data while milling a workpiece. At several points, the workpiece was damaged and this induced a certain sensor data time series. Due to noise and since its a real world measurement, ...
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1answer
23 views

How to interpret the original time series approximated using principal component analysis? [closed]

I've read some posts about PCA applied on time series, but still a bit confused and I have the following questions(Suppose I am working with a time series of the return of 50 industries and I want to ...
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1answer
36 views

Do Recurrent Neural Networks assume stationarity or just a general kind of sequential dependence?

Just when I thought I had convinced myself that RNNs make no other assumption about a sequence other than that there are dependencies between the inputs and that (in the case of monodirectional RNNs) ...
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1answer
104 views

Plot overlapping time series [closed]

I'm trying to plot my test set and test set predictions to check the differences and see how my autoencoder reconstructed the data, but since I have a test set 30x10 I have a huge visualization ...
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1answer
31 views

Finding an appropriate binary classification algorithm for time series data intervals

Maybe someone here has experience in this matter and can point me in the right direction. I want to classify parts of an interval of numerical movement data as either resting or not resting. I have ...
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20 views

estimating single or multiple model for Multiple Time Series Forecasting

I am a newbie in the ML field. So please, neglect or better correct, if I am wrong somewhere. I am working on a requirement where details of loading time for each page/component will be given. Now I ...
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5 views

Selecting time window for very different feature lengths

I'm doing my first steps with python, keras and a 1D time series classification. I can train a model, save it, apply it to validation or test data. I'll build my first implementation around this ...
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22 views

PyTorch LSTM with varying time steps

Is it possible to create an LSTM in PyTorch where the time steps are varying? For example, heights where measurements are taken at various times. The data might look like this: Person id Inches tall ...
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17 views

AI with metadata and timeseries inputs

In data science you sometimes encounter a scenario where you have meta data on a given process and the process data itself. For example you have a mechanical component that is tested over time. So you ...
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1answer
24 views

Predict the first observations of a time series when order of the model is higher

Suppose you have you have a time series with 365 observations, one for each day of the year, and you split the first 183 rows in training set and the latest 182 in test set. Suppose you create an AR (...
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25 views

How can i find the accurcy for time series? [closed]

I have to work for the first time with time series and I have some question about this interesting field of machine learning. What I have to do: I should make a forecast for quantity for some special ...
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38 views

Pytorch: Reduce forward prediction dimensions of GRU network / Improving Network Architecture

I am currently working on a GRU network to predict a time series, please note that I am completely new to machine learning and pytorch. Also I have never had a formal education in programming. This ...
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1answer
32 views

Is there potentially data leakage during imputation for time-varying sensor data?

I have a time-varying dataset that contains some missing data. I have sensors that continuously monitor some properties at evenly-spaced intervals and I would like to impute the missing values using ...
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8 views

Hodrick–Prescott filter on train/test split

I have a multivariate time series $X$ which has been time-based split to $X_{train}$ and $X_{test}$. If I wanted to do standard scaling without data leak, it is possible to learn the scaling ...
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28 views

Time Series Classification & Seasonal Data [closed]

I am looking to predict values based on seasonal data. Bonuses are paid quarterly/ annually/ monthly and amount usually goes up after couple of time periods. Data is given below. I have converted the ...
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45 views

Time series forecasting. How use future values

I have a time series dataset containing hourly data from a few year, like below. Let's assume that I want to make prediction for the next 3 hours (2021-01-01 19:00, 2021-01-01 20:00, 2021-01-01 21:00)....

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