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
4k views

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
251 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
75 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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1answer
62 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
859 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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21 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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87 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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202 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
172 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
29 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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13 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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13 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
74 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
680 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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17 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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26 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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65 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
622 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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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
50 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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1answer
134 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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25 views

Getting KeyError while executing forcast( ) in statsmodel's holtwinters function

I'm trying to get time series prediction using the following code. ...
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1answer
63 views

I have hourly data of a metric for 15 days, Can i predict the outcome values for same metric for the next 15 days?

I have tried a linear regression model for the same data, Since the regression line is continuous i'm not sure if it works to predict the outcome values for next 15 days, or for a given period of time!...
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206 views

Use both differencing and normalization in time series modeling to make it stationary?

I am working on a time series dataset. Should we use both differencing and normalizing or either of the ones to make it stationary?
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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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1answer
570 views

what is the complexity of a bidirectional recurrent neural network?

In particular, what is the complexity of a bi-directional recurrent neural network taking into account the variants of LSTM and GRU as well for training? I am hoping if I can get links to some ...
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1answer
116 views

Relationship between two continuous variables in time series data

I have a dataset that collects daily data based on transactions between two entities. I wish to find the strength, direction, and kind of relationship between two continuous variables i.e. Number of ...
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1answer
38 views

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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1answer
39 views

modeling time series data with large number of variables

I want to model time series data of 52 dependent variable using neural networks in order to forecast these series in future . I have tried some architectures of LSTM and CNN (conv1D) models but my ...
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1answer
97 views

How to test dev set on Time Series data via forecasting

I'm implementing $3$ Bayesian Deep Learning models (links below) for my masters. I'm supposed to test them on a civil engineering time series data. My models ...
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1answer
14 views

Time Series Target variable taken at much lower sample rate than input features

I have a regression problem that involves predicting a patient's blood pressure from a range of vital sign readings including PTT, PPG, and HR. Each of these input features has been taken at the same ...
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39 views

LSTM Many to one with multiple time steps for time series (multi class classification)

I want to do a time series multi-class classification for fault detection and diagnosis with time-series sensor data set which contains a sequence of 50 records of normal data and sequences of another ...
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1answer
246 views

Can we do multivariate time series analysis using holt-winter ( Exponential smoothing) method?

Just like we have a method like ARIMAX and SARIMAX where we can provide exog and endog variable for perfroming multivariate analysis. I was hoping is there a way, we can achieve same using ETS as well....
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1answer
39 views

Formulate multivariate multistep time series forcasting using traditional machine learning, NOT deep learning

How do you represent multivariate multistep data using traditional machine learning? I know this seems like a tailored problem for RNN/LSTM, but I am wondering what the alternative machine learning ...
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12 views

Implementing mean shift clustering in spatio-temporal domain?

We used meanshift clustering in the spatio-temporal domain (i.e., [x, y, t] with a kernel of size [32, 32, 200]). We treat clusters with at least 2 samples as fixations and use cluster center as the ...
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1answer
3k 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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1answer
32 views

Should I shuffle my `train_test_split` if my time series contains lagged features?

I understand that it is not recommended to shuffle your training and test sets for time series, else the model will not be able to understand the time dependency of the features. However, I am now ...
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1answer
88 views

LSTM Target Is Also One of It's Inputs?

I have two input arrays that include both historical and forecasted data, and one input array that is only historical. I'm trying to predict (or "forecast") the latter array given the ...
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1answer
380 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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0answers
24 views

Cross correlation

I am trying to find a good algo (low latency) that is able to take two time series and determine which one is leading on the other one if any. The time series do not necessarily have the same ...
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0answers
34 views

Multiple Entities, Multivariate, Multi-step - Time Series Prediction - Python

My goal is to create a time series model with Multiple Entities - I have multiple products with pre orders and they all have the a similar bell shaped curve peeking at the release date of the product ...
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0answers
13 views

How can i improve my Bidirectional LSTM timeseries forecasting

I am trying to forecast a timeseries and I am using LSTM for it. But the forecast outside train data is pretty bad. I tried adding layers, changing epochs but couldn't improve. The forecast is ...
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1answer
54 views

How to distinguish between normal fluctuation and outliers in ARIMA model?

I have a dataset about sales per day of certain products at the ITEM/DAY/STORE level , I've plotted the series and visually examined it for any outliers, volatility, or irregularities. And this is ...
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1answer
291 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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1answer
32 views

How to predict orders with a range of items? And total orders which sum up to the total?

So I do have data like this: With the help of distinct order IDs, I can figure out how many orders are there and from units shipped, I can get the number of items in the order. Now I want to predict ...
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3answers
44 views

How to use machine learning to find pattern of similar regions in signals

I have a long time series signal. This signal is usually very stable, but it will change when the sensor is stimulated, and this change is usually very short. I know this can be trained using the ...
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1answer
143 views

Training data : forecasted or actual?

I am working on a time series prediction problem. I am using keras models for machine learning. For this prediction, weather variables are used as input. They can be of two types: forecasted and ...
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4answers
20k views

Looking for a good package for anomaly detection in time series

Is there a comprehensive open source package (preferably in python or R) that can be used for anomaly detection in time series? There is a one class SVM package in scikit-learn but it is not for the ...
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1answer
61 views

Is my dataset unlearnable, or is my LSTM model not smart enough?

I have time-series data obtained from a video. The data is composed of bitrate and corresponding label pairs for each timestamp: The distribution over the first 30 seconds is as follows: I have ...
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1answer
14 views

How to compare error metrics for model with and without Seasonality?

I am aiming to guage the difference in my model performance from using data with and without Sesonality removal. My approach to Seasonality removal is taking the log of the column data and then ...

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