Questions tagged [forecasting]

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11
votes
1answer
9k views

How to Predict the future values of time horizon with Keras?

I just built this LSTM neural network with Keras ...
9
votes
1answer
4k views

Can Reinforcement learning be applied for time series forecasting?

Can Reinforcement learning be applied for time series forecasting?
4
votes
1answer
268 views

Forecasting via LSTM or XGBoost… is it really a forecast or

I guess I understand the idea of predictions made via LSTM or XGBoost models, but want to reach out to the community to confirm my thoughts. This tutorial does a nice job explaining step by step of ...
4
votes
1answer
1k views

Time series forecasting using multiple time series as training data

I am trying to forecast the total attendance (ie. the number of entrances, which is also the number of tickets bought) of a festival just two days after it started. That is, knowing how many people ...
4
votes
2answers
331 views

Time series forecasting dilemma. Could feature engineering overcome time dependency?

I keep reading articles about time series forecasting. They all start from the same assumption: time series forecasting can't be treated as a regression/classification problem. It is time dependent, ...
4
votes
2answers
968 views

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 ...
3
votes
1answer
117 views

When forecasting time series, how does one incorporate the test data back into the model after training?

When you build a classification or regression model, you typically split the data into a train data set and a test data set. The test data is a randomly selected subset of the overall data. Once you ...
3
votes
2answers
2k views

VAR model ValueError: x already contains a constant

I'm using VAR model for multivariate time series. The structure is that although each variable is a linear function of past lags of itself and past lags of the other variables, one and/or two of the ...
3
votes
1answer
583 views

how to decide categorical variables for prediction

I have a dataset that contains weekly sales for stores and categories. It looks like this: I would like to apply gradient boosting method to predict weekly sales. My question is: Should I create ...
3
votes
1answer
95 views

What are the prerequisites before running Holt Winters Model?

I just read Demand-Driven Forecasting: A Structured Approach to Forecasting(Wiley and SAS Business Series) and have a few doubts in Holt-Winters Model: 1) Unlike OLS Regression Modeling technique or ...
3
votes
1answer
82 views

Is an Arma model equivalent to a 1-layer Recurrent Neural Network without activation function?

Given a time series $f(t)$ to forecast, let us consider an Arma model of the form: $$ f(t) = c + \sum_{i=1}^p a_i f(t-i) + e(t) + \sum_{j=1}^q b_j e(t-j) $$ where $e(t)$ are the forecast errors. On ...
2
votes
1answer
86 views

Aggregation of Discount

I am trying to predict sales quantity of an item based on their attributes. Discount is one of those attributes. The problem is I am having different discounts in same period for same item .I need to ...
2
votes
1answer
744 views

How to predict NaN (missing values) of a dataframe using ARIMA in Python?

I have a dataframe df_train of shape (11808, 1) that looks as follows: ...
2
votes
1answer
146 views

How to predict next year's gross revenue given this year's data?

I have the following dataset (.csv format) which contains: 100 columns: $\textbf{Period}$ (in years, e.g. 2017, 2018, ..., 2028), $\textbf{Gross Revenue}$, $\textbf{Region}$ (e.g. APAC, NEMEA, etc), ...
2
votes
1answer
40 views

Finding Outliers in Resource Forecast Data

Unsure if this is the correct place to place this, please close if so. I'm a workforce analyst at a large retail company, I own and maintain all the forecasting for our retail stores. This is based ...
2
votes
1answer
308 views

Time Series Forecasting for Multiple Customers using one RNN

I have a product which has univariate and also multivariate time series data from multiple customers. I have variable amount of data available. Ranging between couple of years to couple of months. ...
2
votes
1answer
66 views

how to predict content based demand

this is my first post at ds StackExchange, so please be gentle and let me know if something is not clear :) I have many products (>1M), and I save all the products purchases in a DB with a time stamp....
2
votes
1answer
120 views

Forecasting energy consumption with no historical data when there is a trend

I want to forecast new customers' energy consumption. Let's say I can construct a set of attributes to describe new and existing customers (e.g. size of business, type of business etc.) and I have the ...
2
votes
1answer
471 views

demand forecast for B2B

I am attempting to create a demand forecasting model in python to predict future sales of a particular category of product, using historical sales data. We are a B2B company, which means that we ...
2
votes
0answers
35 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
votes
0answers
36 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
votes
0answers
28 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 "...
2
votes
0answers
21 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
votes
1answer
32 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
votes
1answer
2k views

Is there an R tutorial of using LSTM for multivariate time series forecasting?

There is a great blog post about how to use keras stateful LSTM in R to forecast sunspots. I applied it to financial ts data ...
2
votes
0answers
2k views

Monthly trend with fb prophet

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 ...
2
votes
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
votes
0answers
515 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
votes
1answer
140 views

LSTM regression bias increases when targets go close to 0

I've build a LSTM model for time series forecasting. Results are not bad, with a mean normalized error of 7%. However, this normalized bias shows a clear pattern: The closer to 0 the value to predict, ...
2
votes
1answer
99 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 ...
2
votes
0answers
3k views

Error when using seasonal arima in python

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2
votes
2answers
140 views

How do I arrange my data to predict 6 weeks of daily sales

I have a data.table base that has many variables to use them to forecasting sales for the next 6 weeks of daily sales. In fact, all the database is arranged by date as you can see here.Note that here ...
1
vote
1answer
3k views

time series forecasting - sliding window method

I have been trying to understand this sliding window technique but to no avail and really unsure as to how I would implement it. My dataset: I have hourly values for the electric load for a year (...
1
vote
2answers
73 views

Time-series forecasting

Here is the data: ...
1
vote
1answer
48 views

Time Series Forecasting Seasonal type

As we all know there are two types of seasonal types, additive and multiplicative, but I have trouble telling them apart. To my understanding, in multiplicative seasonality, the magnitude of a ...
1
vote
2answers
993 views

LSTM future steps prediction with shifted y_train relatively to X_train

I'm trying to predict simple one feature time series data with shifted train data. The source looks like this: ...
1
vote
2answers
380 views

Input for Sales Forecasting

I want perform demand forecast for particular item based on attributes.Did I need to train the model with unsold items ? by maintaining sales Quantity as zero or go with only items sold in training ...
1
vote
1answer
20 views

Forecasting - 3 independent variables

I have 3 independent input variables: time, temperature and pressure, and one output - usefulness. Example: Number of the sample; Time; Temperature; Pressure; Usefulness 1;1;30;50;9% 2;3;50;50;80% ...
1
vote
1answer
46 views

Time Series Forecasting

I work in the Oil & Gas industry. I have been trying to build a ts forecasting model with covariates, and the model R code is as follows: ...
1
vote
1answer
49 views

How could I improve my FB Prophet forecast?

I've got 1325 days of revenue data and when plotting the components it makes 100% sense from a domain expert point of view, so the model is capturing the variations quite well (or it seems it does...)....
1
vote
1answer
349 views

How to calculate customer purchase interval and predict next purchase in python?

Suppose we have a data set consists of columns TransactionId, CardNo, TransactionDate then how can we calculate the customer purchase interval (means if customer A purchased on Jan 1st and after ...
1
vote
2answers
50 views

How to predict weather? [closed]

I want to know how does one predict day to day weathers. Like what are the factors that must be known to predict day to day weathers using a NN. Few factors I could think of are: Humidity Weather ...
1
vote
1answer
35 views

Last cell in recurrent network always the most accurate

I am using a recurrent network for time series forecasting. The prediction from the last cell in the network always seems to be the most accurate. For example if I have 20 cells (so my input samples ...
1
vote
1answer
305 views

Long term time series forecasts with small dataset

I have a small dataset which has timestamp and temperature values for 6 months(I.e. one temperature value per day). I would like to forecast 2-3 months of temperature. I would like to know, what kind ...
1
vote
1answer
182 views

Choosing the right model for predicting demand

We have a data set of 300,000+ records that looks something like the following: ...
1
vote
0answers
16 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 ...
1
vote
0answers
13 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 ...
1
vote
0answers
26 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 ...
1
vote
0answers
11 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 ...