Questions tagged [time-series]

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

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1answer
14 views

Is it common to add noise to Time Series data before training a model

I once read about somebody who added noise to their time series before training a model. They didn't write why they did it though. Is this common practice? If it is, why do people do it ie. to ...
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1answer
20 views

Time series analysis

I'm developing a prediction model that can predict the number of patients that will be admitted at a particular time in a hospital. My dataset has details like admission date, admission time and ...
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1answer
14 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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12 views

How to generate sports tracking data using deep learning?

Data: I have a 2D Numpy array that contains tracking data for football. Each row has the (x,y) coordinates for all players + the ball. That's 22 players and 1 ball = 46 columns. The frequency is 0.1 ...
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1answer
19 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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1answer
28 views

Getting monthly revenue predictions for outlets

I am often presented with a task of predicting monthly revenues of retail outlets. Say I have a training set of N outlets, each associated with a series of historical monthly revenues (target) and a ...
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1answer
14 views

Forecasting Consumption for Multiple Products for Multiple Regions

Came across a very interesting Real-World Time Series Forecast Problem. Can you please help me understand the right track to resolve the below Time Series problem: Input Data Sample: and we want to ...
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2answers
74 views

Determining if a time series is random

An example time series would be the stock market, which is sometimes described as a random walk. Over time, this is clearly not the case as it has essentially gone in one direction (up) with only ...
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1answer
25 views

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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1answer
15 views

Split time series by python or by keras?

In Python you can use TimeSeriessplit() to split a time series properly for training but you can also do the same(?) in Keras by TimeseriesGenerator. Which one is recommendable? And/or what are ...
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1answer
6 views

Cluster evolution over time

I have a dataset of transactional data with customer ID and I want to segment the dataset into groups using cluster analysis. I'm interested in following the evolution of each cluster over time, but ...
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8 views

LSTM based anomaly detection scheme too closely tracking long spans of anomalous points

I've built a time series anomaly detection process that accurately predicts the value at the next interval. However, when there are dozens of anomalous events in a row, the model starts to "catch up" ...
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57 views

Building a speech commands dataset for audio recognition applications

I'm working on a DL project to recognize (10 - 15) Arabic speech commands from a continuous stream of audio, and I want to create a dataset similar to Google's Speech Commands dataset. Fortunately, I ...
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22 views

Forecasting futures prices [closed]

I was hoping someone could point me to some skeleton code, algorithm, or other nudge in the right direction for dealing with futures predictions. This is somewhat of a tricky problem, because it ...
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1answer
12 views

How to construct validation set for time series for NN?

I would like to train my model with a validation set. As the data is a time series I have to use timeseriessplit: ...
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1answer
47 views

Autoencoders for the compression of time series

I am trying to use autoencoder (simple, convolutional, LSTM) to compress time series. Here are the models I tried. Simple autoencoder: ...
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0answers
9 views

What are the algorithm for temporal prediction taking into account ponctual events

I would like to know what's are the main approach for events prediction. Let's say there are 10 potential events recorder over a period of 10 years. What would be the algorithm to predict those event ...
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2answers
25 views

Classify if someone is home based on time

I have a dataset with locations and a timestamp of a subject. For each location and timestamp I determined by comparing the location to the home address if the subject was at home or not (0/1) and ...
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1answer
30 views

Forecasting time series outside the training/test set

I am trying to predict some time series based on precedent values using LSTM. I have pretty good results when I compare the predicted time series with the test set (0,18% error) I just miss how to ...
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25 views

Indicator for target variable in logistic regression

I am trying to predict the probability of an event occurrence for different entities based on historical time series data. The event is binary (0, 1) and monthly snapshots are available. I am setting ...
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8 views

What are good public datasets for time series analysis with “certified” (by papers in the literature) good predictors of the target variable?

I have to test different models for time series forecasting and predictors (exogenous covariates) goodness evaluation and I would like to use datasets used in relevant scientific publications that ...
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1answer
23 views

Predicted and true values distributions comparison

Is this alarming when a distribution of predicted values differs from a distribution of true values? I use xgbregressor and get the following plots Usage of boxcox doesn't improve the case. My data ...
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1answer
58 views

XGBoost vs ARIMA for Time Series analysis

Doing time series analysis, I have doubts on choosing the right model. I want to predict the next 30 mins window, from the input dataset which contains the no. of error count for that particular 1 min ...
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1answer
20 views

Predict items customers would buy in next order

I am working on a time series classification problem to identify what items customers would buy in their next order (customers orders different products every week). Let's say we have a customer who ...
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2answers
47 views

How to normalize a data set of multiple time series?

I have the a data set representing the electricity consumption of 25 000 customer. The electricity readings are taken from each smart meter each 15 min for a period of 3 days. The data is takes from ...
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1answer
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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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18 views

How to understand logistic trend parameters proposed by Prophet (Facebook)?

I read the paper and I saw that the logistic trend is defined as below : $$ g(t) = \frac{C(t)}{1 + \exp{ -k(t) (t - m(t)) }} $$ Where $k$ and $m$ are respectively a growth rate and an offset parameter....
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Time Series forecasting with machine learning

I have collected some data which basically encapsulates some internet traffic behaviour like average packet delivery time between two sensors, queue lengths, etc. at different times of the day and ...
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2answers
24 views

How to read specific time for specific value row by row using python

Here I have a dataset with date,time and one input column. This input column is included with values 3 and 4. Here I want to read the datetime value which is having 4 row by row. Other rows which is ...
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3answers
95 views

References for longitudinal data analysis

So my goal is to study longitudinal data (data in time series) by applying some data mining techniques. Ultimately I want to be able to "predict" outcomes. For example, a study of patients along the ...
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2answers
8 views

Scaling of the Streaming Data

I have to make prediction on streaming data for which I have trained the model offline. Is it a common practice to use scaling when we have to train the model offline and have to predict the on ...
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0answers
17 views

LSTM forcasting: time series as input and a unique value for each as output

I am sorry for the mistakes I will make in this post, I'm new to deep learning ^^' I am trying to build an LSTM model that can help me predict some unique value according to time series indices and ...
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23 views

Detecting abundance of a certain periodic pattern in a time series?

I am really stumped at the moment about how to solve a particular problem. I have many time series like this: This represents the number of hours a person spends on a website each day throughout the ...
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10 views

How SARIMA predictions works

I am doing practice on SFO air traffic dataset and trying to forecast air traffic for 48 months ahead using SARIMA. The optimal SARIMA parameters are order=(2,1,2) and seasonal_order=(0,1,2,12), ...
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2answers
30 views

Time series modelling

I have daily data for 2.5 years , but with more data points as 0, so when i excluded them in the cases which seems to be invalid. Can i use any other model than models used in time series or should i ...
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1answer
31 views

LSTM to multivariate sequence classification

How can I train multivariate to multiclass sequence using LSTM in keras? I have 50000 sequences, each in the length of 100 timepoints. At every time point, I have 3 features (So the width is 3). I ...
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1answer
8 views

Recording changing values over time

I am somewhat confident this is the correct approach but I need a little sanity check before setting out on this. I want to record a numerical value for a category over time. There will be many ...
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19 views

Deep learning, signal processing and feature engineering

I have a signals represented in python in dense matrices (the values are y-coordinates from a chart - eg. weather temp etc. in different locations around the world). I'm currently trying to process/...
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27 views

How to use TimeDistributed fo CNN+LSTM?

I am trying to classify 6 classes time-frequency domain signal (STFT spectrogram) with a size of 3601x217 pixels. Assume that for each classes have 70 training samples, 20 validation samples, and 10 ...
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1answer
17 views

Predicting complete time series for given parameter sets

Im searching for an approach that is able to predict a complete time series for a given parameter set. Imagine a robotic arm which has a starting position and a target position. There is a sensor ...
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32 views

Problem with Prophet Model regressior

I am working on predicting half-hourly UK electricity prices with prophet. I have two other time series: gas prices and initial national demand out-turn. So, after merging all the data-sets together ...
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0answers
7 views

Loss and accuracy remains constant in time series classification by LSTM

I have a time series data with a classification label of 1 and 0. I am using a LSTM model to classify the series by taking 100 consecutive timestamps as input with a single label. Even after training ...
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1answer
29 views

Machine learning for missing data in time series

We have two time series columns - column A is the reference column ( source of truth) and column B is a ''cousin'' of column A, in the sense that it exhibits ( or should exhibit) the same patterns, ...
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1answer
34 views

Do timesteps must have the same temporal distance in training a RNN?

I have a recurrent neural network with LSTM units that I want to train with batches of 6 timesteps. Each timestep is a record of a dataset and represents the temporal aggregation over 5 minutes of ...
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1answer
19 views

Sliding window approach using SVR & LightGBM

I'm working on a multivariate time series forecast using a couple of ML algorithms (Neural Networks, Support Vector Machines & Gradient boosting algorithms). I need to measure the performance of ...
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1answer
70 views

2D-Input to LSTM in Keras

I have following problem: I would like to feed LSTM with train_datagen.flow_from_directory The input is basically a spectrogram images converted from time-series into time-frequency-domain in PNG ...
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1answer
38 views

What is an appropriate machine learning technique to analyse development of status over time? [closed]

I have a dataset as follows (not the actual data, but representative): ...
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9 views

Context Dataset in Time Series Forecasting

In time series forecasting, what is context dataset? How different is it from the actual training dataset and what role does it play either in training or validation?
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How to combine data having similar distribution?

I have a collection of time series data with data points of around 2 years of daily data. I am thinking of a way to increase the number of data points in it so that the neural network gets a better ...
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32 views

Influence of trend on (supposedly) correlated time series

TL;DR: What is the impact of a linear trend on the correlation between time series that are (most likely) not spuriously correlated? I'm currently trying to reconstruct/cross-validate an analysis ...