Questions tagged [forecasting]

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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 ...
2
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0answers
18 views

How to automate Seasonal Arima?

I am building Seasonal Arima for more than 10k products. In all the tutorials and blogs mentioned, I need to do the exploration to find the p,d,q values along with seasonality value using the ...
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0answers
69 views

ARIMA: How to understand performance of the model?

I am new to use of ARIMA model and after working on it for a couple of days and doing research - I'm not sure how to interpret the performance of my model... Here is what the ...
2
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0answers
44 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 ...
2
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0answers
36 views

Time Series and Statistical Learning methods differences

I am trying to understand the Time Series and Forecasting methods. I have this basic theoretical question about this: Why "Time Series" is NOT included on the Contents of, for example, the book "...
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0answers
22 views

Why might an LSTM be capable of predicting an ARMA signal but not a linear combination of ARMA signals?

I have an LSTM network and am testing it on some dummy ARMA signals. I'm trying to predict the signal 5 time steps into the future. The network is capable of outperforming Naive (persistence) when ...
2
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1answer
44 views

Building a time-series demand forecasting model

I am forecasting demand for certain types of goods and services, which I expect to be correlated to a sub-set of a basket of macroeconomic indicators (considering 15-20 indicators) I do not know ...
2
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0answers
34 views

Can I forecast with discontinued data using ARIMA?

I have data for sales on monthly basis, but a few months' information is not in the CSV file or data file. Can I forecast or fill that missing month with other calculated values from present records? ...
2
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0answers
1k views

Understanding how to use ConvLSTM for multistep ahead forecasting

I have a problem where I have transaction data for many banking accounts. The task is to train a model on historical debit/expense transactions and then forecast expense transactions for the next n ...
2
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0answers
673 views

Recommended model for univariate or multivariate multistep ahead time series forecasting

I have a dataset consisting of recurring and non-recurring expense transactions from bank accounts, as well as other features describing the bank account and each transation. I aggregate these ...
2
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1answer
108 views

How do I use rnn to forecast to n periods with limited data?

So this is my 1st time trying to run a small time-series dataset through an RNN, but after a lot of searching, I haven't been able to find, 1. How I can use this to forecast to n periods ? (like in ...
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0answers
4k views

Error when using seasonal arima in python

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0answers
10 views

Web scraping using Beatiful Soup

I have this code and I wanna extract holidays, petrol and temperature but I don't know where is the problem. I need your help as soon as possible, please. I want to add this extraction to my dataset ...
1
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1answer
35 views

What are some good methods to forecast future revenue on categorical and value based data?

I have monthly snapshots (3 years) of all the contract data. It includes following information: Contract status [Categorical]: Proposed, tracked, submitted, won, lost, etc Contract stages [...
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1answer
25 views

best NN architecture for point prediction

I'm training to predict a single value y (continuos in [0,1]) based on a number of variables ...
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1answer
24 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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0answers
22 views

Multi-dimensional Time Series Features

I am new to applying ML to time series data but I do have experience doing general supervised learning. I have time series that is multidimensional (so several variables over time) with one output ...
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0answers
9 views

lags number in multivariate time series analysis correlation

I am trying to calculate the correlation coefficient(Pearson's r) of a financial time series $Y(t)$ and other exogenous variables $X_1(t),..., X_n(t)$. I am trying to understand the impact of my ...
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0answers
25 views

Aggregate results in time series forecasting

I'm working on time series forecasting with some sales data, with no exogenous variables, only sales per day. After some analysis, including seasonal decompose, plot autocorrelation and parcial ...
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0answers
11 views

Forecast multiple unevenly spaced time series

I am building a time-series forecasting model to predict some patterns in climatological data. The dataset consists of many (2 mln) time series which look for example as: However the observations ...
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0answers
49 views

State of the Art/Research 2020 of Time Series Forecasting/Prediction

Im looking for the state of the art/research of time series data for forcasting/prediction. As far as im aware it is Extrem Gradient Boosting (XGBoost) or LSTM (neuronal networks) or are there other ...
1
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1answer
78 views

Python: forecast unevenly spaced time-series?

My data has timestamps corresponding to the failure occurrences of a specific component in machinery. The timestamps are not uniformly distributed. My question is: 1) what methods can I use to (almost)...
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0answers
13 views

open source time series sales data for forecasting

I'm looking for open source time series sales data (past 2-3 yrs or more) that contains at least the following variables. ...
1
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1answer
40 views

Forecasting with a Machine Learning Algorithm

Im sorry if it is a too general question, but i am stuck somewhere between perfect and adequate in my model. So, i wanted to ask here. If it is not a suitable question, your negative feedbacks are all ...
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0answers
14 views

How to forecast timeseries based on different events?

I have a few IoT sensors around my house that over time store some events with timestamps. Each sensor has a unique type e.g. ‘front’ or ‘back’. Let’s call this set X. Now I have one sensor which ...
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0answers
20 views

Time Series Forecasting with RNN/LSTM/NARX

I have some experimental datasets (like 4 or 5), and each dataset has three time series data, say $u1(t)$, $u2(t)$, and $x(t)$. The three time series of each experiment are similar but not the same. ...
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0answers
18 views

Vector Autoregression forecasting with large dataset

I am trying to use VAR to forecast electricity price for a whole day and I have a dataset with over 20000 observations (price for every hour) from 2015-2017. My first intuition was to select 19975 ...
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0answers
17 views

Does the R hts-Package work with time series of different units?

currently I'm having a look at the wonderful hts-Package. Its idea is to forecast hierarchical time series (like described here). In this example the unit of measurement across all time series is the ...
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0answers
67 views

Linear correlation and XGboost regression for time series

I am working with sales time-series data, I have a history of 9 years of monthly data. I am trying to forecast sales for the next 12 months. I am using XGboost regression to build multivariate time ...
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0answers
211 views

Does the add_regressor method on Facebook Prophet also work with categorical variables?

I went through the documentation of Facebook Prophet and was able to build a similar model for my time series dataset. The additional regressors I used were numeric. I achieved a reasonable MAPE score....
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0answers
12 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
66 views

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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0answers
14 views

Cross-validation for Timeseries Counterfactual Analysis

We are looking to predict counterfactual states from time-series data. In our problem we are looking to determine the energy savings from a grid-installed device that is varied on and off for many ...
1
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1answer
617 views

Forecasting multiple time series with a single model

I have a dataset with sales numbers for ~500 different markets (assume different cities or regions) and need monthly forecasts for each market. Instead of building 500 different models, I'm interested ...
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0answers
53 views

Is it possible to use LSTM for time series forecasting for future months with test data having only NaN values?

my dataset is a univariate time series with one column as months, other column having demands for the corresponding months. My test dataset has NaN vals only for all the months. Can LSTM be used in ...
1
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1answer
173 views

How to split a dataset into train and test sets for time series (multiple step-multiple output forecasting)?

I am trying to use an LSTM neural net to do multiple step / multiple output forecasting (I predict multiple values in one time knowing some values in the past). But, I have realized that I must be ...
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0answers
80 views

Finding p values and estimates of external variables in facebook Prophet for forecasting in Python

I am using facebook Prophet for multivariate forecasting which has an objecting of forecasting prices. My target variable is affected by n number of external variables too. I am using add_regressor ...
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0answers
150 views

Generalization of RNN/LSTM/GRU… model

Given a time-series prediction with a Recurrent Neural Network (doesn't matter if LSTM/GRU/...), a forecast might look like this: to_predict (orange) was fed to the model, predicted (purple) is the ...
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0answers
23 views

What approach should I take to model forecasting problem in machine learning?

I have a dataset which contains 4000k rows and 6 columns. The goal is to predict travel time demand of a taxi. I have read many articles regarding how to approach the problem. So, every writer tell ...
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0answers
241 views

How to apply an RNN to forecast non-stationary time series?

Is it possible to predict a time series which is non-stationary, in the sense that, the dependent variable Y have an increasing trend? Therefore, the highest value of $Y$ in the training set may be ...
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0answers
200 views

predictive clustering trees in Python?

I am faced with a time series forecasting cold-start problem, specifically I am forecasting energy consumption of businesses where historic consumption data is available only for training but not new ...
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1answer
106 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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0answers
112 views

What's the difference between ELM and NNAR?

I'm working with time series forecasting using the two techniques that involve neural networks, the Extreme Learning Machine and the Autoregressive Neural Network. Reading the two methodologies, the ...
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0answers
87 views

How to model non-linear demand function?

I am trying to build a dynamic pricing algorithm on intermittent data (a lot of zeros between non-zero values). I have on average 100 non-zero data points for each product. However, it seems to be ...
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0answers
36 views

forecast product demand in one week using machine learning approach

I'm trying to predict product demand in store. The predictors I have include price, competitor's price, store ID, date. My target variable is sales volume (in a particular store). What I need to ...
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0answers
43 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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0answers
212 views

Predict the probability that a customer buy today

My company sells a single product that is a commodity. If you buy it, I can be sure that you will buy it again in the near future from me or from my competitors. The demand is affected by the weather. ...
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0answers
41 views

What values should I choose for P and Q?

I am trying to forecast energy data generated by LoadProfileGenerater. This data is generated every half hour for 2 years. I am following this tutorial. I have checked it for stationarity using ...
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0answers
396 views

Spark fitting into Data Science Paradigm for timeseries data

I have seen Dataframe as new API on Spark2 instead of RDD.So I have following few question about the utility of Spark in terms of time series data. Is forecasting still limited to memory available in ...
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0answers
10 views

Backtesting Windows

I am looking to use an auto ML platform for retail forecasting. Will use 3.5 years of sales data. Our business has changed significantly. Higher margins, fewer incentives and competition has ...