1, the time series basic rules of law - Cycle Factor Method
- Periodicity extraction time series prediction reference: Time sequence rule Quick Start Method
- Factor calculation cycle factors
- Calculation base
- Forecast = base * factors
- Observation sequence, a periodic sequence, when present, can be used as a baseline period Factor Method
- Tianchi in the competition - capital inflow and outflow forecast - Challenges Baseline- Tianchi contest - Ali cloud Tianchi , cycle factor can get 110 points + score, ranking 500 into properly properly.
2, the linear regression - time characteristics using linear regression do
- The periodical characteristics as time characteristics, then each training set sample is "characterized Time -> Target Value", the time dependence sequence is removed, the sliding window need not be strictly dependent on the training samples taken. Common is the time to express 0-1 dummy variable, following several characteristics:
- In order to convert the week 0-1 variables, from Monday to Sunday day, one-hot encoding a total of seven variables
- 0-1 into the variable holidays, as the number of holidays, can be simply divided into two categories, "there holiday" - "no holiday", a total of two hot encoded variables; or impart different coding values, such as the distinction Day, Year , Labor Day, etc. using 1,2,3
- Early into the binary variables, two types represented simply as "a month" - "non-month", wherein a total of 2
- Similar months, can be converted to 0-1 early in the variable
- Time granularity of control, distinguish weekday or weekend
- Observation sequence, a periodic sequence, when present, may be used as baseline linear regression
- Tianchi in the competition - capital inflow and outflow forecast - Challenges Baseline- Tianchi contest - Ali cloud Tianchi , linear regression can get 100 + results, should not to 500, and more features will be able to go under the regulation.
Linear Model 3, a conventional sequence modeling method, ARMA / ARIMA like. reference:
- Addressed to your Financial Time Series Analysis: Basics
- Autoregressive / moving average order determination Identifying the orders of AR and MA terms in an ARIMA model include the 11 general principle, which refers to:
- Differential method eliminates the introduction of a positive correlation but at the same time negatively correlated
- AR term positive correlation can be eliminated, MA term to eliminate negative correlation
- AR and MA term action items will cancel each other out, can be an attempt to reduce the time usually contains two elements, to avoid over-fitting
4, the decomposition of time series, using an adder or a multiplicative model models original sequence split into four parts.
- Split into four parts: a long-term trend of change T, seasonal variation S (explicit period, fixed amplitude, period length fluctuation), cyclic variation C (implicit cycle, the long period of non-rigid rules fluctuation) and irregular variation I. reference:
- SCI in the multiplicative model are proportional, additive model of SCI and T have the same dimension.
- C is more complex cyclic variations, short-term trends do not reflect or fall in.
- Two types of smoothing method:
- Decomposed to extract the trend of moving average smoothing method seasonal_decompose simplicity. statsmodels.tsa.seasonal.seasonal_decompose
- STL in a robust locally weighted regression smoothing decomposition method. statsmodels.tsa.seasonal.STL
- Seasonal analysis. Data, seasonal factors, compared with the overall trend was relatively weak. Original: Investigating Seasonality in Time Series A: A Mystery in Three Parts ; Chinese: Dry | seasonal analysis was not simple, careful not to random data in the analysis, the seasonal
- Tianchi in the competition - capital inflow and outflow forecast - Challenges Baseline- Tianchi contest - Ali cloud Tianchi , time-series decomposition method can also be achieved very good results. (Behind have the opportunity to try this method)
5, wherein the project started, the sliding window of time to change the data is organized using xgboost / LSTM model / time convolutional networks. reference:
- kaggle merchandise sales forecasting 1st idea: Feature Project LGBM + / LSTM, 1st Place Solution | Kaggle
- kaggle merchandise sales forecasting 5th idea: Feature Project LGBM + / CNN-DNN / seq2seq, 5th Place Solution | Kaggle
6, the data set into supervised learning using xgboot / LSTM model / time convolutional network / seq2seq (attention_based_model). reference:
- How time sequence into a supervised learning problem with Python - Cloud + Community - Tencent cloud
- Multivariate time series prediction Keras LSTM in with the - cloud + Community - Tencent cloud
- Time convolution Network (TCN) Summary: timing model is no longer the world Recurrent Network (RNN), but as a way to extract information rude, do not myth CNN!
- Seq2seq algorithm can be introduced on the model of the mechanism of attention, seen purely seq2seq solution, combined with attention mechanisms we have not seen the open source code (which may be searched carefully enough).
- seq2seq代码：Kaggle-Competition-Favorita/seq2seq
- Attentional mechanisms Information:
- NTU - CHANG "Attention_based_model"
- They only said attentional mechanisms (Attention Mechanism) do not practice, or I'll give you the code line and explain
- Shengyuan car: a true model fully graphical Seq2Seq Attention
- "Attention is All You Need" shallow reading (Introduction + Code)
- Trantor scholars: Attention mechanism to explain (b) - Self-Attention and Transformer
- The Illustrated Transformer
- Visualizing A Neural Machine Translation Model (Mechanics of Seq2seq Models With Attention)
7, Facebook-prophet, similar to the STL decomposition idea, because I think in the degree of control and interpretability than the traditional timing models have an advantage, so a separate train. reference:
- Official website Description (English)
- Official website notbook (English)
- Chinese Recommended Articles from principle to use are introduced, it is conscience. CHANG: study Facebook's time series forecasting algorithm Prophet
- Understand, would like to further good use, you can take a look at papers and official website, the time line and over the python source code
- Prior_scale understand how to control the trend item in the code, seasonal items and holiday items
- For Trend parameters changepoint_range, changepoint_prior_scale model fitting and how it affects the degree of generalization
- Uncertainty-Intervals (interval_width parameters) Trend of how to use the results to predict
- Papers in "Simulated Historical Forecasts" corresponding to the prophet's Diagnostics tool, you can use the tool to do time-series cross-validation of the accuracy of the evaluation model, how to use this tool to adjust the model
8, depth of the learning network, in conjunction with CNN + RNN + Attention, each with a different co-ordination. Currently only read the paper, we have the code given the way the code link, the code did not look.
The main design philosophy:
- CNN capture the short-term local dependencies
- RNN capture long-term macro-dependency
- Attention significant period or weighted variables
- AR-scale change data capture (not too get to know what the meaning of ~)
method:
- LSTNet : suitable for autocorrelation time series chart exhibits a significant period, otherwise fairly traditional methods. Pytorch-LSTNet , LSTNet-Keras , LSTNet-gluon (Mxnet) .
- LSTM-TPA : Improved attention mechanism, focusing on selected key variables, rather than selecting the time step; the experimental effect is not obvious to say the period of the time series can have a good effect. TPA-LSTM-Tensorflow
Code
- LSTNet interpretation of the code, BINGO Hong: Detailed LSTNet
- TPA-LSTM attention mechanism, BINGO Hong: TPA attention mechanism (TPA-LSTM)
Author: BINGO Hong
link: https: //zhuanlan.zhihu.com/p/67832773