Questions tagged [time-series]

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

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384 views

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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49 views

Train/validation/test and cross-validation on panel dataset

(Cross-posting a previous question from CrossValidated in case it is more suitable here: Train/Validation/Test and Cross-Validation on Panel Dataset ) I have a panel dataset, indexed by $Year$ and $...
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1answer
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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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143 views

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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235 views

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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21 views

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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229 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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31 views

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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26 views

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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537 views

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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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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154 views

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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66 views

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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27 views

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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108 views

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
99 views

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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49 views

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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1answer
83 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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19 views

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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2answers
162 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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8 views

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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1answer
60 views

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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1answer
15 views

Architectures that take inputs of mixed sampling rates

Let's say a model is trained on multiple datasets of 1D time series. These datasets have been gathered with different sampling rates. I plan to use a convolution neural network to process these time ...
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1answer
347 views

ARIMA training super slow

I am fitting ARIMA model (from statsmodels) on 20 000 elements dataset on a 24 CPU 200+GB RAM cloud server for over 24 hours now. It loads all the CPU's. But It takes so long... Is it how it works or ...
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22 views

Is there a way to calculate the cosine distance between 2 time series?

Let's say I have time series data of City A, City B, City C & City D that looks like this: ...
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1answer
19 views

(Labeled, if possible) time-series datasets for anomaly detection [closed]

I would like to create a big list of available time-series datasets for anomaly detection. I'm especially interested in the following: The time-series data should be segmented into cycles Ideally, ...
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1answer
20 views

How to detect time for the future events in time series data?

I am dealing with IOT data from a mechanical machine. On the input I have ~100 features that are measured every minute. On the output, I have labels of zeros and ones, where zero indicates the absence ...
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67 views

How to predict future prices with Keras LSTM time-series prediction model?

I have a trained and tested LSTM model which is meant to predict Ethereum close prices using all time csv data (24h steps). How do I now go about inputting an empty dataframe with future dates to ...
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52 views

Rolling window on uneven time series classification

I have a univariate time series data that I would like to take about 60 seconds of, extract features using tsfresh and classify into multiclass. So I might end up with a dataframe like: ...
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13 views

Prevent model from predicting the same examples for different inputs

I have a ANN model, that predicts a fixed length curve. The problem is, those curves are really similar to each other, for example these two: To compare those curves, I use RMSD between their points....
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11 views

Modelling probability of switching of label from 0 to 1 in next time period for multivariate time series data

I have a data set consisting of unbalanced panel data, i.e. longitudinal multivariate measurements on multiple individuals. I want to estimate a probability that the individual will become 1 in next ...
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1answer
46 views

Creating features from raw accelerometer data

I have a dataset containing raw 3-axis accelerometer data collected from a users lower leg and I want to create a classification model (as simple as possible) that detects if the user is sat down or ...
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8 views

Series Through a GPU's Window To, For Each Item, Output a Prediction and Retrain?

Perhaps I'm missing something obvious but I've not run across a Keras or PyTorch example of online training and series prediction loop implemented on a GPU with these (seemingly obvious) ...
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7 views

How to cluster large time series?

I want to perform clustering on a dataset which has a few thousand stock prices time series, each one with daily prices for previous 5 years (a couple thousand days). What would be the best approach ...
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1answer
28 views

Terminology of time series

The terms Time Series Analysis, Time Series Forecasting, and Time Series Modeling are widely ...
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2answers
30 views

Time Series data visualization

When I visualize data using matplotlib it displays very well, but when using Plotly, the data display very bad.
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10 views

Correcting for seasonality on multiple timelines

I have many time-series of economic data, broken down quarterly. There is always an element of seasonality in each timeline, but it can very wildly. See the below charts: I'd like to correct for ...
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1answer
20 views

Time series forecasting with constraints

I want to predict the passenger flow volume of an airline route, which subjects to supply capacity constraints of the route (i.e., the passenger flow volume should not be higher than the supply ...
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23 views

How far into the future can I forecast a time-series with an LSTM and strongly seasonal data

I am working on a Sequence-to-Sequence + Attention model for some time-series data. Now I have a really long time series, basically 40 years of daily observations for multiple sensors. The data itself ...
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1answer
46 views

PyTorch: LSTM for time-series failing to learn

I'm currently working on building an LSTM network to forecast time-series data using PyTorch. I tried to share all the code pieces that I thought would be helpful, but please feel free to let me know ...
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8 views

Forecasting monthly visitor count from daily values

I have a dataset of the daily visitor count of a website. Given this information, I want to forecast what the monthly visitor count will be. Depending on the visitor count on a day of the month, I ...
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1answer
311 views

Calculate the similarity between pairs of time series data

I have 5 pieces of time series data. It is the weekly sales of 5 different stores (A,B,C,D,E). There are no missing values. A quick visual inspection shows that these 5 pieces of time series data have ...
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7 views

Audio generating models

I was looking for an implementation of audio\timeseries generation models. I want a model that utilizes time series as well as a frequency map (such as log-Mel or STFT). My goal is to generate a time ...
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8 views

how to codify date-time feature in a dense vector?

if we have datetime as a feature for our model in time-series analysis (e.g. trading analysis ...). As you know any datetime-based features have two characteristics: cyclic linear For instance if we ...
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1answer
147 views

Segmentation of time series into one-second non-overlapping windows [closed]

I need to split the time series dataset (accelerometer values (timestamp, X, Y, Z)) into segments in the form of a one-second window that does not overlap. I am trying to find an example of its ...
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23 views

Multiple (>1500) timelines- which model to use?

I'm new to Data Science (<6 months). I'm currently working with American economic data from the BLS. I only have quarterly numbers for about 20 years, so my timeline data isn't very wide- but I do ...
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84 views

AR coefficients are not stationary

I have a timeseries data and I want to forecast it by applying ARIMA. After reading data, I decomposed it to analyze its components and get an idea whether it is stationary or not. It seems there is ...
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1answer
99 views

LSTM model prediction is almost constant

I am new to RNN and LSTM and currently experimenting with different settings. When trying to model time series data in absolute terms (predicted close price), I am faced with the following problems: ...

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